Atividade: Regressão para preços de alugueis e venda de imóveis em São Paulo¶

D2APR – Aprendizado de Máquina e Reconhecimento de Padrões D2TEC - Tecnologias de Big Data

IFSP Campinas

Grupo:

  • Evandro Costa Ferreira (CP3021947)
  • Jaqueline Jana da Silva (CP3021891)
  • João Pedro de Oliveira Ferreira (CP3021696)

📊 1. Análise Exploratória¶

O arquivo considerando neste notebook encontra-se aqui;

https://www.kaggle.com/datasets/argonalyst/sao-paulo-real-estate-sale-rent-april-2019

In [1]:
## Rode o código abaixo, apenas se for necessário instalar os pacotes

#Instalando os pacotes necessários
# %pip install botocore==1.33.4
# %pip install pandas
# %pip install seaborn
# %pip install matplotlib
# %pip install numpy
%pip install folium
# %pip install scikit-learn
# %pip install sklearn
# %pip install IPython
%pip install xgboost
%pip install optuna
%pip install lightgbm
# %pip install pickle
%pip install awswrangler
# %pip install fsspec
# %pip install s3fs
# %pip install sagemaker
# %pip install numexpr

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In [2]:
# Importando as bibliotecas necessárias
import awswrangler as wr
import boto3
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mat
import numpy as np
import folium
from folium import plugins
from sklearn.model_selection import train_test_split
from IPython.display import display
from sagemaker import get_execution_role
from io import StringIO

/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).
  from pandas.core.computation.check import NUMEXPR_INSTALLED

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml
In [3]:
# Definindo Função da análise descritiva

def analise_desc(data_treino, data_treino_0, data_treino_1, data_treino_2, data_treino_3, data_treino_4, coluna):

    # Obtendo os descritivos para cada base
    desc_treino = data_treino[coluna].describe().to_frame()
    desc_treino_0 = data_treino_0[coluna].describe().to_frame()
    desc_treino_1 = data_treino_1[coluna].describe().to_frame()
    desc_treino_2 = data_treino_2[coluna].describe().to_frame()
    desc_treino_3 = data_treino_3[coluna].describe().to_frame()
    desc_treino_4 = data_treino_4[coluna].describe().to_frame()

    # Renomeando as colunas para "Todas as Classes", "Centro", "Leste", "Norte", "Oeste" e "Sul"
    desc_treino.columns = ['Todas as Regiões']
    desc_treino_0.columns = ['Centro']
    desc_treino_1.columns = ['Leste']
    desc_treino_2.columns = ['Norte']
    desc_treino_3.columns = ['Oeste']
    desc_treino_4.columns = ['Sul']

    # Concatenando os dataframes em um único dataframe
    desc_concatenado = pd.concat([desc_treino, desc_treino_0, desc_treino_1, desc_treino_2, desc_treino_3, desc_treino_4], axis=1)

    return desc_concatenado.round(2)
In [4]:
# Definindo Função que faz bloxplot

def Box_Plot(data_treino, data_treino_0, data_treino_1, data_treino_2, data_treino_3, data_treino_4, nome_coluna):

    # Criando a figura e os subplots
    fig, axs = plt.subplots(1, 6, figsize=(12, 4))

    # Função para adicionar as indicações de quartis nos boxplots
    def add_quartile_annotations(ax, data, delta, lim_sup, lim_inf):

        q1 = np.percentile(data, 25)
        median = np.percentile(data, 50)
        q3 = np.percentile(data, 75)

        q1_lim = (q1-lim_inf)/delta
        median_lim = (median-lim_inf)/delta
        q3_lim = (q3-lim_inf)/delta

        ax.text(0.05, q1_lim-0.05, f'Q1: {q1:.2f}', transform=ax.transAxes, ha='left')
        ax.text(0.1, median_lim+0.01, f'Mediana: {median:.2f}', transform=ax.transAxes, ha='left')
        ax.text(0.05, q3_lim+0.05, f'Q3: {q3:.2f}', transform=ax.transAxes, ha='left')

    # Plotando o primeiro boxplot (base treino)
    ax0 = sns.boxplot(y=data_treino, ax=axs[0], color = '#CCCCCC')
    axs[0].set_title('Todas as regiões')

    # Plotando o segundo boxplot (base treino_0)
    ax1 = sns.boxplot(y=data_treino_0, ax=axs[1], color = '#98FB98')
    axs[1].set_title('Centro')

    # Plotando o terceiro boxplot (base treino_1)
    ax2 = sns.boxplot(y=data_treino_1, ax=axs[2], color = '#ADD8E6')
    axs[2].set_title('Leste')

    # Plotando o quarto boxplot (base treino_2)
    ax3 = sns.boxplot(y=data_treino_2, ax=axs[3], color = '#FFC0CB')
    axs[3].set_title('Norte')

    # Plotando o quarto boxplot (base treino_3)
    ax4 = sns.boxplot(y=data_treino_3, ax=axs[4], color = '#FFD700')
    axs[4].set_title('Oeste')

    # Plotando o quarto boxplot (base treino_4)
    ax5 = sns.boxplot(y=data_treino_4, ax=axs[5], color = '#A9A9A9')
    axs[5].set_title('Sul')

    # Configurando as legendas dos eixos
    for ax in axs:
        ax.set_ylabel(nome_coluna)
        ax.set_xlabel('')
        ax.set_ylim(ax0.get_ylim())

    x, y = ax0.get_ylim()

    add_quartile_annotations(ax0, data_treino, delta = y-x, lim_sup = y, lim_inf =x)
    add_quartile_annotations(ax1, data_treino_0, delta = y-x, lim_sup = y, lim_inf =x)
    add_quartile_annotations(ax2, data_treino_1, delta = y-x, lim_sup = y, lim_inf = x)
    add_quartile_annotations(ax3, data_treino_2, delta = y-x, lim_sup = y, lim_inf = x)
    add_quartile_annotations(ax4, data_treino_3, delta = y-x, lim_sup = y, lim_inf = x)
    add_quartile_annotations(ax5, data_treino_4, delta = y-x, lim_sup = y, lim_inf = x)

    # Exibindo o gráfico
    plt.tight_layout()
    plt.show()
In [5]:
# Definindo Função que cálcula a distribuição dos dados, considerando o District_Zone como agrupamento.

def Distribuicao(base_a, base_b, nome_base_a, nome_base_b, coluna, rotulo):

  # Média de valores da coluna informada, por zona para Base A
  mean_base_a = base_a.groupby('district_zone')[coluna].mean()
  zones_base_a = mean_base_a.index
  mean_values_base_a = mean_base_a.values
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))


  # Média de tamanho por zona para Base B
  mean_base_b = base_b.groupby('district_zone')[coluna].mean()
  zones_base_b = mean_base_b.index
  mean_values_base_b = mean_base_b.values
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

  fig, axs = plt.subplots(1, 2, figsize=(12, 6))  # Cria uma figura com 2 subplots

  # Plota o gráfico de barras para a Base A no primeiro subplot
  bars1 = axs[0].bar(zones_base_a, mean_values_base_a, color=colors_base_a(np.arange(len(zones_base_a))))
  axs[0].set_xlabel('Zona do Distrito')
  axs[0].set_ylabel(f'Média de {rotulo} ({nome_base_a})')
  axs[0].set_title(f'Média de {rotulo} por Zona do Distrito ({nome_base_a})')

  # Adiciona rótulos para o primeiro gráfico
  for bar, label in zip(bars1, mean_values_base_a):
      axs[0].text(bar.get_x() + bar.get_width() / 2 - 0.1, bar.get_height() + 0.5, str(round(label, 2)), fontsize=10, ha='center')

  # Plota o gráfico de barras para a Base B no segundo subplot
  bars2 = axs[1].bar(zones_base_b, mean_values_base_b, color=colors_base_b(np.arange(len(zones_base_b))))
  axs[1].set_xlabel('Zona do Distrito')
  axs[1].set_ylabel(f'Média de {rotulo} ({nome_base_a})')
  axs[1].set_title(f'Média de {rotulo} por Zona do Distrito ({nome_base_b})')

  # Adiciona rótulos para o segundo gráfico
  for bar, label in zip(bars2, mean_values_base_b):
      axs[1].text(bar.get_x() + bar.get_width() / 2 - 0.1, bar.get_height() + 0.5, str(round(label, 2)), fontsize=10, ha='center')

  plt.tight_layout()
  plt.show()
In [6]:
# Definindo a função que cria um mapa centrado em São Paulo

def Mapa(base):

  sp_map = folium.Map(location=[-23.5505, -46.6333], zoom_start=11)

  # Adicionar marcadores de calor
  heat_data = [[row['latitude'], row['longitude'], row['price'], row['district_zone']] for index, row in base.iterrows()]

  # Adicionar cores de acordo com a região
  color_dict = {
      'Leste': 'blue',
      'Centro': 'green',
      'Sul': 'red',
      'Oeste': 'purple',
      'Norte': 'orange',
  }

  for lat, lon, price, region in heat_data:
      folium.CircleMarker(
          location=[lat, lon],
          radius=5,
          color=color_dict.get(region, 'gray'),  # Usar cinza para regiões não mapeadas
          fill=True,
          fill_color=color_dict.get(region, 'gray'),
          fill_opacity=0.7,
          popup=f"Região: {region}<br>Preço: R$ {price:.2f}",
      ).add_to(sp_map)

  # Adicionar um mapa de calor
  heat_df = pd.DataFrame(heat_data, columns=['latitude', 'longitude', 'price', 'district_zone'])
  heat_df = heat_df.dropna(subset=['latitude', 'longitude', 'price'])
  heat_data = heat_df[['latitude', 'longitude', 'price']].values

  sp_map.add_child(plugins.HeatMap(heat_data, radius=15))

  # Exibir o mapa na tela
  display(sp_map)
In [7]:
def GraficoBarrasEmpilhadas(base, titulos, feature, rotulo):

  # Calcular a média por região para cada DataFrame
  mean_by_region = [df.groupby('district_zone')[feature].mean() for df in base]

  # Definindo as cores
  colors = sns.color_palette("Set1", n_colors=2)

  # Plotando os gráficos de barras empilhadas lado a lado
  fig, axs = plt.subplots(1, 2, figsize=(14, 6))

  for i, df in enumerate(base):
      counts = df.groupby(['district_zone', feature]).size().unstack(fill_value=0)
      counts.plot(kind='bar', stacked=True, color=colors, ax=axs[i], alpha=0.7)

      # Adicionar média por região
      for region, mean in mean_by_region[i].items():
          total = counts.loc[region].sum()
          count_with = counts.loc[region, 1]
          count_without = counts.loc[region, 0]
          percent_with = (count_with / total) * 100
          percent_without = (count_without / total) * 100

          # Anotar dentro da barra (Sim)
          axs[i].annotate(f'{count_with} \n ({percent_with:.2f}%)',
                          xy=(list(counts.index).index(region), count_with / 2),
                          xytext=(0, 1), textcoords='offset points', ha='center', fontsize=10, color='black')

          # Anotar dentro da barra (Não)
          axs[i].annotate(f'{count_without} \n ({percent_without:.2f}%)',
                          xy=(list(counts.index).index(region), ((count_with + count_without) /2 + count_with)),
                          xytext=(0, 1), textcoords='offset points', ha='center', fontsize=10, color='black')


      axs[i].set_xlabel('Região do Distrito', fontsize=12)
      axs[i].set_ylabel(f'Quantidade de imóveis', fontsize=12)
      axs[i].set_title(f'Quantidade de imóveis por Região ({titulos[i]})', fontsize=14)
      axs[i].set_xticklabels(counts.index, rotation=45, fontsize=10)
      axs[i].legend(title=f'{rotulo}', labels=['Sim', 'Não'], fontsize=10, title_fontsize=10)
      axs[i].grid(axis='y', linestyle='--', alpha=0.6)

  plt.tight_layout()
  plt.show()
In [8]:
def GraficoBarrasLadoALado(base, titulos, feature, rotulo):
  # Calcular a média por região para cada DataFrame
  mean_by_region = [df.groupby('district_zone')[feature].mean() for df in base]

  # Definindo as cores
  colors = sns.color_palette("Set1", n_colors=2)

  # Plotando os gráficos de barras lado a lado
  fig, axs = plt.subplots(1, 2, figsize=(14, 6))

  for i, df in enumerate(base):
    counts = df.groupby(['district_zone', feature]).size().unstack(fill_value=0)

    # Coordenadas x para as barras "Sim" e "Não"
    x = np.arange(len(counts.index))

    # Largura das barras
    bar_width = 0.35

    # Criar barras para "Sim" e "Não" lado a lado
    axs[i].bar(x - bar_width/2, counts[1], bar_width, color=colors[0], alpha=0.7, label='Sim')
    axs[i].bar(x + bar_width/2, counts[0], bar_width, color=colors[1], alpha=0.7, label='Não')

    axs[i].set_xlabel('Região do Distrito', fontsize=12)
    axs[i].set_ylabel(f'Quantidade de imóveis', fontsize=12)
    axs[i].set_title(f'Quantidade de imóveis por Região ({titulos[i]})', fontsize=14)
    axs[i].set_xticks(x)
    axs[i].set_xticklabels(counts.index, rotation=45, fontsize=10)
    axs[i].legend(title=f'{rotulo}', labels=['Sim', 'Não'], fontsize=10, title_fontsize=10)
    axs[i].grid(axis='y', linestyle='--', alpha=0.6)

    # Adicionar porcentagens em cima das barras
    for xi, region in enumerate(counts.index):
      total = counts.loc[region].sum()
      count_with = counts.loc[region, 1]
      count_without = counts.loc[region, 0]
      percent_with = (count_with / total) * 100
      percent_without = (count_without / total) * 100

      axs[i].annotate(f'{percent_with:.2f}%', xy=(x[xi] - bar_width/2, count_with), ha='center', fontsize=10, color='black')
      axs[i].annotate(f'{percent_without:.2f}%', xy=(x[xi] + bar_width/2, count_without), ha='center', fontsize=10, color='black')

  plt.tight_layout()
  plt.show()
In [9]:
def GraficoDispersao(base_a, base_b, titulo, xlabel, ylabel):
  plt.figure(figsize=(10, 6))  # Define o tamanho do gráfico

  # Cria o gráfico de dispersão
  plt.scatter(base_a, base_b, alpha = 0.5)
  plt.title(titulo)
  plt.xlabel(xlabel)
  plt.ylabel(ylabel)

  plt.show()
In [10]:
# Printando função com o score, média e desvio padrão
def display_scores(scores):
    print("Scores:", scores)
    print("Mean:", scores.mean())
    print("Standard deviation:", scores.std())

Importando os dados pré-processados¶

O intuito é prevermos os valores de venda e aluguel de imóveis, em São Paulo, considerando as bases separadas previamente separadas.

Sendo assim, vamos ler as base de dados e mostrar parte dos dados

In [11]:
def get_data(bucket_name, response):
    objects = response.get('Contents', [])
    csv_files = [obj['Key'] for obj in objects]
    # Lendo os arquivos CSV e concatenando os resultados
    dfs = []

    for csv_file in csv_files:
        # Obter o conteúdo do arquivo
        response = s3_client.get_object(Bucket=bucket_name, Key=csv_file)
        content = response['Body'].read().decode('utf-8')

        # Verificar se o conteúdo não está vazio
        if content.strip():  # Verifica se o conteúdo não é apenas espaços em branco
            # Ler o conteúdo como DataFrame
            df = pd.read_csv(StringIO(content))
            dfs.append(df)

    if dfs:
        base_input = pd.concat(dfs, ignore_index=True)
        return base_input
    else:
        return None
In [12]:
# Obter o role de execução do SageMaker
role = get_execution_role()

# Nome do bucket e caminho para a pasta
bucket_name = 'input-propriedades'
folder_path_aluguel = 'processed/aluguel'
folder_path_venda = 'processed/venda'

# Configurar o cliente S3
s3_client = boto3.client('s3')

# Listar objetos no caminho especificado
response_aluguel = s3_client.list_objects(Bucket=bucket_name, Prefix=folder_path_aluguel)
response_venda = s3_client.list_objects(Bucket=bucket_name, Prefix=folder_path_venda)

## Puxando os dados de aluguel
base_aluguel = get_data(bucket_name, response_aluguel)
print(f'\n BASE ALUGUEL: \n\n {base_aluguel.head()}')

## Puxando os dados de venda
base_venda = get_data(bucket_name, response_venda)
print(f'\n BASE VENDA: \n\n {base_venda.head()}')

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml

 BASE ALUGUEL: 

    price  elevator  furnished  size district_zone  toilets property_type  \
0   1500         0          0   230         Leste        2     apartment   
1   1000         1          0    55         Leste        2     apartment   
2   1300         1          1    50         Leste        2     apartment   
3   4500         1          0    87         Leste        2     apartment   
4    900         0          0    35         Leste        2     apartment   

   condo  swimming_pool  rooms negotiation_type  parking  suites  new  
0   2893              0      3             rent        2       1    0  
1    395              1      2             rent        1       1    0  
2      0              1      2             rent        1       1    0  
3   1100              0      2             rent        1       1    0  
4      0              0      1             rent        1       1    0  

 BASE VENDA: 

      price  elevator  furnished  size district_zone  toilets property_type  \
0   522900         1          0    83         Leste        2     apartment   
1   420000         1          1    54           Sul        2     apartment   
2  1400000         1          0    83         Oeste        3     apartment   
3   530000         0          0   107         Leste        2     apartment   
4   579000         0          0    63           Sul        2     apartment   

   condo  swimming_pool  rooms negotiation_type  parking  suites  new  
0    700              1      3             sale        2       1    0  
1   1200              1      2             sale        1       1    0  
2    800              1      2             sale        2       2    0  
3   1300              0      2             sale        1       1    0  
4    390              1      2             sale        1       1    0

Em nossa base de dados temos as seguintes colunas:

  • Price: Preço final anunciado (R$ Reais Brasileiros)
  • Condo: Despesas do condomínio
  • Size: Tamanho da propriedade em metros quadrados (m²) (apenas áreas privativas)
  • Rooms: Número de quartos
  • Toilets: Número de banheiros
  • Suites: Número de quartos com banheiro privativo
  • Parking: Número de vagas de estacionamento
  • Elevator: Valor binário: 1 se houver elevador no prédio, 0 caso contrário
  • Furnished: Valor binário: 1 se a propriedade estiver mobiliada, 0 caso contrário
  • Swimming Pool: Valor binário: 1 se a propriedade tiver piscina, 0 caso contrário
  • New: Valor binário: 1 se a propriedade for muito nova, 0 caso contrário
  • District: O bairro e a cidade onde a propriedade está localizada
  • Negotiation Type: Venda ou Aluguel
  • Property Type: O tipo de propriedade
  • Latitude: Latitude
  • Longitude: Longitude

Essa informação consta no catálogo de dados, gerado pelo GLUE.

In [13]:
base_aluguel.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14038 entries, 0 to 14037
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   price             14038 non-null  int64 
 1   elevator          14038 non-null  int64 
 2   furnished         14038 non-null  int64 
 3   size              14038 non-null  int64 
 4   district_zone     14038 non-null  object
 5   toilets           14038 non-null  int64 
 6   property_type     14038 non-null  object
 7   condo             14038 non-null  int64 
 8   swimming_pool     14038 non-null  int64 
 9   rooms             14038 non-null  int64 
 10  negotiation_type  14038 non-null  object
 11  parking           14038 non-null  int64 
 12  suites            14038 non-null  int64 
 13  new               14038 non-null  int64 
dtypes: int64(11), object(3)
memory usage: 1.5+ MB

Comentário: Note que o dataframe possui 7019 linhas e em nenhuma coluna temos valores nulos puro. Onze colunas são formadas por valores inteiros, quarto são object (string) e duas são valores float (colunas de latitude e longitude).

In [14]:
base_venda.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12604 entries, 0 to 12603
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   price             12604 non-null  int64 
 1   elevator          12604 non-null  int64 
 2   furnished         12604 non-null  int64 
 3   size              12604 non-null  int64 
 4   district_zone     12604 non-null  object
 5   toilets           12604 non-null  int64 
 6   property_type     12604 non-null  object
 7   condo             12604 non-null  int64 
 8   swimming_pool     12604 non-null  int64 
 9   rooms             12604 non-null  int64 
 10  negotiation_type  12604 non-null  object
 11  parking           12604 non-null  int64 
 12  suites            12604 non-null  int64 
 13  new               12604 non-null  int64 
dtypes: int64(11), object(3)
memory usage: 1.3+ MB

Comentário: Note que o dataframe possui 6302 linhas e em nenhuma coluna temos valores nulos puro. Onze colunas são formadas por valores inteiros, quarto são object (string) e duas são valores float (colunas de latitude e longitude).

Todo o pré-processamento, foi realizado via ETL com o GLUE. Logo, iremos trabalhar com a base já tratada.

Análise Visual¶

In [15]:
# Plotando o histograma, para os atributos númericos, da base de vendas.
base_venda.hist(figsize=(20,15))
display()

Comentário: Pelos histogramas, podemos ver que a quantidade de imóveis à venda novos é muito inferir a quantidade de imóveis "antigos". Vemos também que a maioria dos imóveis não está mobiliada, e aproxidamente 2/3 dos imóveis possui elevador.

In [16]:
# Plotando o histograma, para os atributos númericos, da base de aluguéis.
base_aluguel.hist(figsize=(20,15))
display()

Comentário: Pelos histogramas, podemos ver que o comportamento é semelhante ao de imóveis à venda

Separação: Treino e Teste¶

Agora será feita a separação entre base de treino e teste. Iremos utilizar a coluna Distric_Zone como parâmetro para estratificação (será mantida a mesma proporção entre zonas no conjunto de teste e treino)

In [18]:
# Filtrando bases

base_venda_centro = base_venda.loc[base_venda['district_zone'] == 'Centro']
base_venda_leste = base_venda.loc[base_venda['district_zone'] == 'Leste']
base_venda_norte = base_venda.loc[base_venda['district_zone'] == 'Norte']
base_venda_oeste = base_venda.loc[base_venda['district_zone'] == 'Oeste']
base_venda_sul = base_venda.loc[base_venda['district_zone'] == 'Sul']

base_aluguel_centro = base_aluguel.loc[base_aluguel['district_zone'] == 'Centro']
base_aluguel_leste = base_aluguel.loc[base_aluguel['district_zone'] == 'Leste']
base_aluguel_norte = base_aluguel.loc[base_aluguel['district_zone'] == 'Norte']
base_aluguel_oeste = base_aluguel.loc[base_aluguel['district_zone'] == 'Oeste']
base_aluguel_sul = base_aluguel.loc[base_aluguel['district_zone'] == 'Sul']
In [19]:
# Fazendo proporção de 80% para treino e 20% para teste

train_base_venda_centro, teste_base_venda_centro = train_test_split(base_venda_centro, test_size=0.2, random_state=42)
train_base_venda_leste, teste_base_venda_leste = train_test_split(base_venda_leste, test_size=0.2, random_state=42)
train_base_venda_norte, teste_base_venda_norte = train_test_split(base_venda_norte, test_size=0.2, random_state=42)
train_base_venda_oeste, teste_base_venda_oeste = train_test_split(base_venda_oeste, test_size=0.2, random_state=42)
train_base_venda_sul, teste_base_venda_sul = train_test_split(base_venda_sul, test_size=0.2, random_state=42)

train_base_aluguel_centro, teste_base_aluguel_centro = train_test_split(base_aluguel_centro, test_size=0.2, random_state=42)
train_base_aluguel_leste, teste_base_aluguel_leste = train_test_split(base_aluguel_leste, test_size=0.2, random_state=42)
train_base_aluguel_norte, teste_base_aluguel_norte = train_test_split(base_aluguel_norte, test_size=0.2, random_state=42)
train_base_aluguel_oeste, teste_base_aluguel_oeste = train_test_split(base_aluguel_oeste, test_size=0.2, random_state=42)
train_base_aluguel_sul, teste_base_aluguel_sul = train_test_split(base_aluguel_sul, test_size=0.2, random_state=42)
In [20]:
# Concatenando as bases

treino_base_venda = pd.concat([train_base_venda_centro, train_base_venda_leste, train_base_venda_norte, train_base_venda_oeste, train_base_venda_sul], ignore_index=True)
teste_base_venda = pd.concat([teste_base_venda_centro, teste_base_venda_leste, teste_base_venda_norte, teste_base_venda_oeste, teste_base_venda_sul], ignore_index=True)

treino_base_aluguel = pd.concat([train_base_aluguel_centro, train_base_aluguel_leste, train_base_aluguel_norte, train_base_aluguel_oeste, train_base_aluguel_sul], ignore_index=True)
teste_base_aluguel = pd.concat([teste_base_aluguel_centro, teste_base_aluguel_leste, teste_base_aluguel_norte, teste_base_aluguel_oeste, teste_base_aluguel_sul], ignore_index=True)
In [21]:
# Verificando percentual da base de treino para venda

result_venda_treino = treino_base_venda.groupby('district_zone').size().reset_index(name='qtd')
result_venda_treino['percentual'] = round(result_venda_treino['qtd'] / len(treino_base_venda) * 100,2)
result_venda_treino
Out[21]:
district_zone qtd percentual
0 Centro 1420 14.09
1 Leste 3616 35.87
2 Norte 1624 16.11
3 Oeste 1457 14.45
4 Sul 1964 19.48
In [22]:
# Verificando percentual da base de teste para venda

result_venda_teste = teste_base_venda.groupby('district_zone').size().reset_index(name='qtd')
result_venda_teste['percentual'] = round(result_venda_teste['qtd'] / len(teste_base_venda) * 100,2)
result_venda_teste
Out[22]:
district_zone qtd percentual
0 Centro 356 14.11
1 Leste 904 35.83
2 Norte 406 16.09
3 Oeste 365 14.47
4 Sul 492 19.50
In [23]:
# Verificando percentual da base de treino para aluguel

result_aluguel_treino = treino_base_aluguel.groupby('district_zone').size().reset_index(name='qtd')
result_aluguel_treino['percentual'] = round(result_aluguel_treino['qtd'] / len(treino_base_aluguel) * 100,2)
result_aluguel_treino
Out[23]:
district_zone qtd percentual
0 Centro 1140 10.15
1 Leste 2862 25.49
2 Norte 1702 15.16
3 Oeste 2780 24.76
4 Sul 2744 24.44
In [24]:
# Verificando percentual da base de teste para aluguel

result_aluguel_teste = teste_base_aluguel.groupby('district_zone').size().reset_index(name='qtd')
result_aluguel_teste['percentual'] = round(result_aluguel_teste['qtd'] / len(teste_base_aluguel) * 100,2)
result_aluguel_teste
Out[24]:
district_zone qtd percentual
0 Centro 286 10.18
1 Leste 716 25.48
2 Norte 426 15.16
3 Oeste 696 24.77
4 Sul 686 24.41

Comentários: note que os percentuais ficaram bem alinhados entre treino e teste tanto na base de aluguel como na base de venda

📊 1.01 Análise Exploratória - Price¶

Preço final anunciado (R$ Reais Brasileiros)

In [25]:
print("Análise - Preço - Venda\n")
desc_price_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'price')
desc_price_venda

Análise - Preço - Venda
Out[25]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 611281.46 587070.63 381819.55 482829.93 1193673.24 725423.07
std 741720.73 523070.41 495612.60 357103.12 1229070.49 757065.50
min 42000.00 42000.00 45000.00 87000.00 125000.00 98815.00
25% 250000.00 318000.00 200000.00 285000.00 528500.00 280000.00
50% 380000.00 435000.00 265000.00 379000.00 800000.00 455500.00
75% 680000.00 650000.00 400000.00 550000.00 1300000.00 850000.00
max 10000000.00 5000000.00 10000000.00 3490000.00 8500000.00 6950000.00
In [26]:
print("Análise - Preço - Aluguel\n")
desc_price = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'price')
desc_price

Análise - Preço - Aluguel
Out[26]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 3115.05 2922.65 1838.14 1794.21 4611.96 3829.51
std 3618.96 2572.97 1904.76 1045.10 4746.78 4241.02
min 480.00 630.00 480.00 500.00 840.00 700.00
25% 1350.00 1600.00 1100.00 1200.00 2000.00 1600.00
50% 2000.00 2200.00 1400.00 1500.00 3000.00 2400.00
75% 3301.00 3200.00 2000.00 2100.00 5500.00 4000.00
max 50000.00 23000.00 45000.00 15000.00 50000.00 50000.00
In [27]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'price', 'Preço')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

Comentário: Vemos que a maioria dos imóveis que se encontram na região Oeste e Sul, possui uma média de preços, tanto aluguel quanto venda, superior as demais regiões.

📊 1.02 Análise Exploratória - Condo¶

Despesas do condomínio

In [28]:
print("Análise - Condo - Venda\n")
desc_condo_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'condo')
desc_condo_venda

Análise - Condo - Venda
Out[28]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 540.02 618.59 324.90 424.04 963.26 661.20
std 620.34 557.90 326.65 426.41 951.16 696.23
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 160.00 317.25 70.00 0.00 413.00 275.00
50% 400.00 500.00 290.00 398.00 752.00 500.00
75% 700.00 738.50 450.00 588.50 1200.00 850.00
max 7428.00 4874.00 3300.00 3200.00 7428.00 5400.00
In [29]:
Box_Plot(data_treino = treino_base_venda['condo'], data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro']['condo'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste']['condo'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte']['condo'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste']['condo'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul']['condo'], nome_coluna = 'condo')
In [30]:
print("Análise - Condo - aluguel\n")
desc_condo_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'condo')
desc_condo_aluguel

Análise - Condo - aluguel
Out[30]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 829.43 812.53 469.86 594.41 1223.35 958.16
std 839.84 819.25 440.62 444.32 1048.94 908.54
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 398.00 405.00 190.00 392.00 580.00 466.00
50% 600.00 586.50 409.50 505.00 900.00 688.50
75% 1000.00 850.00 600.00 690.00 1500.00 1161.50
max 9500.00 7928.00 4800.00 3508.00 9500.00 8860.00
In [31]:
Box_Plot(data_treino = treino_base_aluguel['condo'], data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro']['condo'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste']['condo'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte']['condo'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste']['condo'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul']['condo'], nome_coluna = 'condo')
In [32]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'condo', 'Preço de condominio')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

Comentário: Vemos que os valores de condominio nas regiões Oeste e Sul, são maiores em relação as demais regiões. O mesmo comportamento visto no preço dos imóveis. Isso era de se esperar: imóveis mais caros, estão em melhores localizações e possuem maior "infraestrutura", aumentando assim seu custo final

📊 1.03 Análise Exploratória - Size¶

Tamanho da propriedade em metros quadrados (m²) (apenas áreas privativas)

In [33]:
print("Análise - Size - Venda\n")
desc_size_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'size')
desc_size_venda

Análise - Size - Venda
Out[33]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 78.70 79.18 64.10 75.26 108.97 85.64
std 50.68 58.42 33.55 43.81 68.09 49.93
min 30.00 30.00 30.00 34.00 30.00 30.00
25% 50.00 45.00 47.00 51.00 66.00 54.00
50% 62.00 59.00 54.00 64.00 88.00 67.00
75% 87.00 91.00 66.00 80.00 130.00 100.00
max 620.00 420.00 343.00 505.00 620.00 360.00
In [34]:
Box_Plot(data_treino = treino_base_venda['size'], data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro']['size'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste']['size'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte']['size'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste']['size'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul']['size'], nome_coluna = 'size')
In [35]:
print("Análise - Size - aluguel\n")
desc_size_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'size')
desc_size_aluguel

Análise - Size - aluguel
Out[35]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 89.84 84.77 69.99 75.35 111.13 100.09
std 64.14 65.91 37.86 44.56 77.40 71.58
min 30.00 30.00 30.00 30.00 30.00 30.00
25% 52.00 45.00 50.00 51.00 62.00 55.00
50% 68.00 60.00 59.00 63.00 85.50 70.00
75% 100.00 90.00 75.00 80.00 140.00 120.00
max 880.00 440.00 443.00 574.00 880.00 670.00
In [36]:
Box_Plot(data_treino = treino_base_aluguel['size'], data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro']['size'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste']['size'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte']['size'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste']['size'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul']['size'], nome_coluna = 'size')
In [37]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'size', 'Tamanho')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))
In [38]:
# Plotando a relação de Tamanho x Preço

# Criando a figura
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Primeiro gráfico: Base Aluguel
sns.scatterplot(x='size', y='price', data=treino_base_aluguel, ax=axes[0])
axes[0].set_title('Tamanho x Preço (Aluguel)')

# Segundo gráfico: Base Venda
sns.scatterplot(x='size', y='price', data=treino_base_venda, ax=axes[1])
axes[1].set_title('Tamanho x Preço (Venda)')

# Ajuste o espaçamento entre os gráficos
plt.tight_layout()

# Exiba a figura
plt.show()

Comentário: Podemos ver que há uma relação direta entre o tamanho do imóvel e seu preço. Imóveis maiores, custam mais caro. O que já era esperado.

📊 1.04 Análise Exploratória - Rooms¶

Número de quartos

In [39]:
print("Análise - Rooms - Venda\n")
desc_rooms_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'rooms')
desc_rooms_venda

Análise - Rooms - Venda
Out[39]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 2.32 1.95 2.25 2.42 2.60 2.43
std 0.72 0.83 0.58 0.61 0.83 0.71
min 1.00 1.00 1.00 1.00 1.00 1.00
25% 2.00 1.00 2.00 2.00 2.00 2.00
50% 2.00 2.00 2.00 2.00 3.00 2.00
75% 3.00 3.00 3.00 3.00 3.00 3.00
max 6.00 4.00 5.00 5.00 6.00 5.00
In [40]:
Box_Plot(data_treino = treino_base_venda['rooms'], data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro']['rooms'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste']['rooms'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte']['rooms'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste']['rooms'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul']['rooms'], nome_coluna = 'rooms')
In [41]:
print("Análise - Rooms - aluguel\n")
desc_rooms_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'rooms')
desc_rooms_aluguel

Análise - Rooms - aluguel
Out[41]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 2.31 1.86 2.23 2.26 2.46 2.44
std 0.83 0.88 0.64 0.72 0.87 0.92
min 1.00 1.00 1.00 1.00 1.00 1.00
25% 2.00 1.00 2.00 2.00 2.00 2.00
50% 2.00 2.00 2.00 2.00 2.00 2.00
75% 3.00 3.00 3.00 3.00 3.00 3.00
max 10.00 4.00 5.00 5.00 7.00 10.00
In [42]:
Box_Plot(data_treino = treino_base_aluguel['rooms'], data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro']['rooms'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste']['rooms'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte']['rooms'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste']['rooms'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul']['rooms'], nome_coluna = 'rooms')
In [43]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'rooms', 'Número de quartos')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

📊 1.05 Análise Exploratória - Toilets¶

Número de banheiros

In [44]:
print("Análise - Toilets - Venda\n")
desc_toilets_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'toilets')
desc_toilets_venda

Análise - Toilets - Venda
Out[44]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.0
mean 2.04 1.74 1.81 2.07 2.52 2.3
std 0.91 0.73 0.72 0.80 1.17 1.0
min 1.00 1.00 1.00 1.00 1.00 1.0
25% 2.00 1.00 1.00 2.00 2.00 2.0
50% 2.00 2.00 2.00 2.00 2.00 2.0
75% 2.00 2.00 2.00 2.00 3.00 2.0
max 7.00 6.00 7.00 6.00 7.00 6.0
In [45]:
Box_Plot(data_treino = treino_base_venda['toilets'], data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro']['toilets'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste']['toilets'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte']['toilets'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste']['toilets'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul']['toilets'], nome_coluna = 'toilets')
In [46]:
print("Análise - Toilets - aluguel\n")
desc_toilets_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'toilets')
desc_toilets_aluguel

Análise - Toilets - aluguel
Out[46]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 2.11 1.74 1.88 1.89 2.41 2.35
std 1.00 0.84 0.66 0.83 1.14 1.15
min 1.00 1.00 1.00 1.00 1.00 1.00
25% 2.00 1.00 2.00 1.00 2.00 2.00
50% 2.00 2.00 2.00 2.00 2.00 2.00
75% 2.00 2.00 2.00 2.00 3.00 3.00
max 8.00 7.00 6.00 8.00 8.00 7.00
In [47]:
Box_Plot(data_treino = treino_base_aluguel['toilets'], data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro']['toilets'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste']['toilets'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte']['toilets'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste']['toilets'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul']['toilets'], nome_coluna = 'toilets')
In [48]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'toilets', 'Qtd. de banheiros')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

Comentários: Vemos que a média nas regiões centro, leste e norte estão bem próximas de 2 banheiros, mas na zona Oeste e Sul, a média está mais próximo de 3. Como a média de quartos é maior nessa região, é de se esperar que o número de banheiros também seja para comportar os moradores.

📊 1.06 Análise Exploratória - Suites¶

Número de quartos com banheiro privativo

In [49]:
print("Análise - Suites - Venda\n")
desc_suites_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'suites')
desc_suites_venda

Análise - Suites - Venda
Out[49]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 0.93 0.64 0.76 0.99 1.28 1.15
std 0.77 0.61 0.64 0.68 0.93 0.85
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 1.00 0.00 0.00 1.00 1.00 1.00
50% 1.00 1.00 1.00 1.00 1.00 1.00
75% 1.00 1.00 1.00 1.00 1.00 1.00
max 6.00 4.00 4.00 4.00 6.00 4.00
In [50]:
Box_Plot(data_treino = treino_base_venda['suites'], data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro']['suites'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste']['suites'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte']['suites'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste']['suites'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul']['suites'], nome_coluna = 'suites')
In [51]:
print("Análise - Suites - aluguel\n")
desc_suites_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'suites')
desc_suites_aluguel

Análise - Suites - aluguel
Out[51]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 1.03 0.67 0.86 0.82 1.26 1.26
std 0.89 0.73 0.62 0.70 1.00 1.02
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 1.00 0.00 1.00 0.00 1.00 1.00
50% 1.00 1.00 1.00 1.00 1.00 1.00
75% 1.00 1.00 1.00 1.00 1.00 1.00
max 5.00 4.00 5.00 4.00 5.00 5.00
In [52]:
Box_Plot(data_treino = treino_base_aluguel['suites'], data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro']['suites'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste']['suites'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte']['suites'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste']['suites'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul']['suites'], nome_coluna = 'suites')
In [53]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'suites', 'Quartos com banheiro')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

Comentários: Podemos notar que nas regiões Centro, Leste e norte, a maioria dos imóveis não possui suites, visto que a média está abaixo de 1

📊 1.07 Análise Exploratória - Parking¶

Número de vagas de estacionamento

In [54]:
print("Análise - Parking - Venda\n")
desc_parking_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'parking')
desc_parking_venda

Análise - Parking - Venda
Out[54]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 1.33 0.96 1.19 1.42 1.70 1.52
std 0.76 0.58 0.57 0.71 0.96 0.84
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 1.00 1.00 1.00 1.00 1.00 1.00
50% 1.00 1.00 1.00 1.00 1.00 1.00
75% 2.00 1.00 1.00 2.00 2.00 2.00
max 7.00 4.00 6.00 6.00 7.00 5.00
In [55]:
Box_Plot(data_treino = treino_base_venda['parking'], data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro']['parking'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste']['parking'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte']['parking'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste']['parking'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul']['parking'], nome_coluna = 'parking')
In [56]:
print("Análise - Parking - aluguel\n")
desc_parking_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'parking')
desc_parking_aluguel

Análise - Parking - aluguel
Out[56]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 1.45 1.10 1.21 1.29 1.75 1.65
std 0.89 0.78 0.63 0.73 0.97 1.01
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 1.00 1.00 1.00 1.00 1.00 1.00
50% 1.00 1.00 1.00 1.00 2.00 1.00
75% 2.00 1.00 1.00 2.00 2.00 2.00
max 9.00 6.00 7.00 5.00 7.00 9.00
In [57]:
Box_Plot(data_treino = treino_base_aluguel['parking'], data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro']['parking'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste']['parking'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte']['parking'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste']['parking'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul']['parking'], nome_coluna = 'parking')
In [58]:
Distribuicao(treino_base_aluguel, treino_base_venda, "Aluguel", "Venda", 'parking', 'Nº de vagas de estacionamento')

/tmp/ipykernel_20710/3799473616.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_a = plt.cm.get_cmap('Set1',len(zones_base_a))
/tmp/ipykernel_20710/3799473616.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  colors_base_b = plt.cm.get_cmap('Set1', len(zones_base_b))

Comentários: Em imóveis maiores, com mais quartos, o número de moradores tende a ser maior, logo é de se esperar a necessidade de mais vagas de estacionamento para imóveis maiores. Vemos que as regiões Oeste e Sul, por possuirem imóveis maiores, também tem uma oferta maior de imóveis com mais vagas, como esperado.

📊 1.08 Análise Exploratória - Elevator¶

Valor binário: 1 se houver elevador no prédio, 0 caso contrário

In [59]:
print("Análise - Elevator - Venda\n")
desc_elevator_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'elevator')
desc_elevator_venda

Análise - Elevator - Venda
Out[59]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 0.41 0.38 0.35 0.45 0.55 0.43
std 0.49 0.49 0.48 0.50 0.50 0.50
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 0.00 0.00 0.00 0.00 0.00 0.00
50% 0.00 0.00 0.00 0.00 1.00 0.00
75% 1.00 1.00 1.00 1.00 1.00 1.00
max 1.00 1.00 1.00 1.00 1.00 1.00
In [60]:
treino_base_venda.groupby(['district_zone', 'elevator']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[60]:
district_zone elevator Qtd.
0 Centro 0 876
1 Centro 1 544
2 Leste 0 2362
3 Leste 1 1254
4 Norte 0 892
5 Norte 1 732
6 Oeste 0 660
7 Oeste 1 797
8 Sul 0 1113
9 Sul 1 851
In [61]:
print("Análise - Elevator - aluguel\n")
desc_elevator_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'elevator')
desc_elevator_aluguel

Análise - Elevator - aluguel
Out[61]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 0.30 0.30 0.29 0.25 0.32 0.34
std 0.46 0.46 0.45 0.43 0.47 0.47
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 0.00 0.00 0.00 0.00 0.00 0.00
50% 0.00 0.00 0.00 0.00 0.00 0.00
75% 1.00 1.00 1.00 0.00 1.00 1.00
max 1.00 1.00 1.00 1.00 1.00 1.00
In [62]:
treino_base_aluguel.groupby(['district_zone', 'elevator']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[62]:
district_zone elevator Qtd.
0 Centro 0 798
1 Centro 1 342
2 Leste 0 2025
3 Leste 1 837
4 Norte 0 1283
5 Norte 1 419
6 Oeste 0 1895
7 Oeste 1 885
8 Sul 0 1818
9 Sul 1 926

Comentário: Como o atributo Elevator é binário, um histograma não nos tratará informações sobre o comportamento dos dados. Plotaremos um gráfico de barras empilhadas para analisar o comportamento e retirar insights.

In [63]:
# Títulos para os gráficos
titulos = ['Aluguel', 'Venda']
bases = [treino_base_aluguel, treino_base_venda]
feature = 'elevator'
rotulo = 'Elevadores'

GraficoBarrasLadoALado(bases, titulos, feature, rotulo)

Comentário: Podemos observar que mesmo em regiões mais caras, a maioria dos imóveis não possui elevador. Essa informação surpreendeu. É de se esperar que em imóveis mais caros, por se tratar de apartamentos, deva ter elevador.

Uma suposição para essa ausência de elevadores é uma quantidade pequena de andares no imóvel (inferior a 4 andares), o que explicaria esse comportamento.

📊 1.09 Análise Exploratória - Furnished¶

Valor binário: 1 se a propriedade estiver mobiliada, 0 caso contrário

In [64]:
print("Análise - Furnished - Venda\n")
desc_furnished_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'furnished')
desc_furnished_venda

Análise - Furnished - Venda
Out[64]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 0.12 0.14 0.11 0.15 0.12 0.11
std 0.32 0.34 0.31 0.35 0.32 0.31
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 0.00 0.00 0.00 0.00 0.00 0.00
50% 0.00 0.00 0.00 0.00 0.00 0.00
75% 0.00 0.00 0.00 0.00 0.00 0.00
max 1.00 1.00 1.00 1.00 1.00 1.00
In [65]:
treino_base_venda.groupby(['district_zone', 'furnished']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[65]:
district_zone furnished Qtd.
0 Centro 0 1228
1 Centro 1 192
2 Leste 0 3236
3 Leste 1 380
4 Norte 0 1386
5 Norte 1 238
6 Oeste 0 1285
7 Oeste 1 172
8 Sul 0 1746
9 Sul 1 218
In [66]:
print("Análise - Furnished - aluguel\n")
desc_furnished_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'furnished')
desc_furnished_aluguel

Análise - Furnished - aluguel
Out[66]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.0 2862.00 1702.00 2780.00 2744.00
mean 0.17 0.2 0.11 0.12 0.23 0.21
std 0.38 0.4 0.32 0.32 0.42 0.41
min 0.00 0.0 0.00 0.00 0.00 0.00
25% 0.00 0.0 0.00 0.00 0.00 0.00
50% 0.00 0.0 0.00 0.00 0.00 0.00
75% 0.00 0.0 0.00 0.00 0.00 0.00
max 1.00 1.0 1.00 1.00 1.00 1.00
In [67]:
treino_base_aluguel.groupby(['district_zone', 'furnished']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[67]:
district_zone furnished Qtd.
0 Centro 0 916
1 Centro 1 224
2 Leste 0 2542
3 Leste 1 320
4 Norte 0 1506
5 Norte 1 196
6 Oeste 0 2148
7 Oeste 1 632
8 Sul 0 2156
9 Sul 1 588
In [68]:
# Títulos para os gráficos
titulos = ['Aluguel', 'Venda']
bases = [treino_base_aluguel, treino_base_venda]
feature = 'furnished'
rotulo = 'Mobilias'

GraficoBarrasLadoALado(bases, titulos, feature, rotulo)

Comentário: Em todas as regiões vemos que mais de 77% delas não está mobiliada. Esse comportamento era esperado.

📊 1.10 Análise Exploratória - Swimming Pool¶

Valor binário: 1 se a propriedade tiver piscina, 0 caso contrário

In [69]:
print("Análise - Swimming Pool - Venda\n")
desc_pool_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'swimming_pool')
desc_pool_venda

Análise - Swimming Pool - Venda
Out[69]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 0.54 0.39 0.47 0.68 0.65 0.60
std 0.50 0.49 0.50 0.47 0.48 0.49
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 0.00 0.00 0.00 0.00 0.00 0.00
50% 1.00 0.00 0.00 1.00 1.00 1.00
75% 1.00 1.00 1.00 1.00 1.00 1.00
max 1.00 1.00 1.00 1.00 1.00 1.00
In [70]:
treino_base_venda.groupby(['district_zone', 'swimming_pool']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[70]:
district_zone swimming_pool Qtd.
0 Centro 0 865
1 Centro 1 555
2 Leste 0 1932
3 Leste 1 1684
4 Norte 0 523
5 Norte 1 1101
6 Oeste 0 515
7 Oeste 1 942
8 Sul 0 783
9 Sul 1 1181
In [71]:
print("Análise - Swimming Pool - aluguel\n")
desc_pool_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'swimming_pool')
desc_pool_aluguel

Análise - Swimming Pool - aluguel
Out[71]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.00 2862.00 1702.00 2780.00 2744.00
mean 0.49 0.32 0.37 0.46 0.60 0.59
std 0.50 0.47 0.48 0.50 0.49 0.49
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 0.00 0.00 0.00 0.00 0.00 0.00
50% 0.00 0.00 0.00 0.00 1.00 1.00
75% 1.00 1.00 1.00 1.00 1.00 1.00
max 1.00 1.00 1.00 1.00 1.00 1.00
In [72]:
treino_base_aluguel.groupby(['district_zone', 'swimming_pool']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[72]:
district_zone swimming_pool Qtd.
0 Centro 0 779
1 Centro 1 361
2 Leste 0 1796
3 Leste 1 1066
4 Norte 0 916
5 Norte 1 786
6 Oeste 0 1117
7 Oeste 1 1663
8 Sul 0 1112
9 Sul 1 1632
In [73]:
# Títulos para os gráficos
titulos = ['Aluguel', 'Venda']
bases = [treino_base_aluguel, treino_base_venda]
feature = 'swimming_pool'
rotulo = 'Piscina'

GraficoBarrasLadoALado(bases, titulos, feature, rotulo)

Comentário: Notamos que para as regiões em que os preços dos aluguéis e venda são mais altos (Zona Oeste e Sul), o percentual de imóveis que possuem piscina são maiores que os que não possuem. O destaque foi para a região norte que possui mais de 70% das casas a venda com piscina

📊 1.11 Análise Exploratória - New¶

Valor binário: 1 se a propriedade for muito nova, 0 caso contrário

In [74]:
print("Análise - New - Venda\n")
desc_new_venda = analise_desc(data_treino = treino_base_venda, data_treino_0 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Centro'], data_treino_1 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Leste'], data_treino_2 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Norte'], data_treino_3 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Oeste'], data_treino_4 = treino_base_venda.loc[treino_base_venda['district_zone'] == 'Sul'],coluna = 'new')
desc_new_venda

Análise - New - Venda
Out[74]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 10081.00 1420.00 3616.00 1624.00 1457.00 1964.00
mean 0.03 0.02 0.04 0.04 0.03 0.02
std 0.17 0.14 0.19 0.18 0.16 0.15
min 0.00 0.00 0.00 0.00 0.00 0.00
25% 0.00 0.00 0.00 0.00 0.00 0.00
50% 0.00 0.00 0.00 0.00 0.00 0.00
75% 0.00 0.00 0.00 0.00 0.00 0.00
max 1.00 1.00 1.00 1.00 1.00 1.00
In [75]:
treino_base_venda.groupby(['district_zone', 'new']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[75]:
district_zone new Qtd.
0 Centro 0 1391
1 Centro 1 29
2 Leste 0 3476
3 Leste 1 140
4 Norte 0 1567
5 Norte 1 57
6 Oeste 0 1417
7 Oeste 1 40
8 Sul 0 1920
9 Sul 1 44
In [76]:
print("Análise - New - aluguel\n")
desc_new_aluguel = analise_desc(data_treino = treino_base_aluguel, data_treino_0 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Centro'], data_treino_1 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Leste'], data_treino_2 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Norte'], data_treino_3 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Oeste'], data_treino_4 = treino_base_aluguel.loc[treino_base_aluguel['district_zone'] == 'Sul'],coluna = 'new')
desc_new_aluguel

Análise - New - aluguel
Out[76]:
Todas as Regiões Centro Leste Norte Oeste Sul
count 11228.00 1140.0 2862.00 1702.00 2780.0 2744.00
mean 0.00 0.0 0.00 0.00 0.0 0.00
std 0.03 0.0 0.04 0.05 0.0 0.03
min 0.00 0.0 0.00 0.00 0.0 0.00
25% 0.00 0.0 0.00 0.00 0.0 0.00
50% 0.00 0.0 0.00 0.00 0.0 0.00
75% 0.00 0.0 0.00 0.00 0.0 0.00
max 1.00 0.0 1.00 1.00 0.0 1.00
In [77]:
treino_base_aluguel.groupby(['district_zone', 'new']).size().reset_index(name='Qtd.').sort_values('district_zone')
Out[77]:
district_zone new Qtd.
0 Centro 0 1140
1 Leste 0 2858
2 Leste 1 4
3 Norte 0 1697
4 Norte 1 5
5 Oeste 0 2780
6 Sul 0 2742
7 Sul 1 2
In [78]:
GraficoBarrasLadoALado([treino_base_aluguel, treino_base_venda], ['Aluguel', 'Venda'], 'new', 'Novo')

Comentário: Na base, a grande maioria dos imóveis não são novos. Pode ser que essa informação não seja interessante para o modelo visto que há concentração em um valor

In [79]:
treino_base_aluguel.columns
Out[79]:
Index(['price', 'elevator', 'furnished', 'size', 'district_zone', 'toilets',
       'property_type', 'condo', 'swimming_pool', 'rooms', 'negotiation_type',
       'parking', 'suites', 'new'],
      dtype='object')

📊 2. Análise de Correlação¶

In [80]:
# Cria uma figura com dois subplots lado a lado
fig, axes = plt.subplots(1, 2, figsize=(20, 7))

colunas = ['price', 'condo', 'size', 'rooms', 'toilets', 'suites', 'parking', 'elevator', 'furnished', 'swimming_pool', 'new']

# Primeiro gráfico: Base de Aluguel
correlation_matrix_aluguel = treino_base_aluguel[colunas].corr()
sns.heatmap(correlation_matrix_aluguel, annot=True, cmap="YlGnBu", ax=axes[1])
axes[1].set_title('Correlação (Aluguel)')


# Segundo gráfico: Base de Venda
correlation_matrix_venda = treino_base_venda[colunas].corr()
sns.heatmap(correlation_matrix_venda, annot=True, cmap="YlGnBu", ax=axes[0])
axes[0].set_title('Correlação (Venda)')

# Ajuste o espaçamento entre os gráficos
plt.tight_layout()

plt.show()

Comentário: Analisando a matriz da esquerda, vemos uma correlação alta entre preço e tamanho, do número de banheiros e quantidade de suites, tamanho e quantidade de vagas

📊 3. Pairplot - Base Venda¶

In [81]:
# Fazendo um pairplot entre as variáveis do dataframe.

sns.pairplot(treino_base_venda[['price', 'condo', 'size', 'rooms', 'toilets', 'suites', 'parking', 'elevator', 'furnished', 'swimming_pool', 'new', 'district_zone']], aspect=1)
Out[81]:
<seaborn.axisgrid.PairGrid at 0x7f121a737e50>

📊 4. Pairplot - Base Aluguel¶

In [82]:
# Fazendo um pairplot entre as variáveis do dataframe.

sns.pairplot(treino_base_aluguel[['price', 'condo', 'size', 'rooms', 'toilets', 'suites', 'parking', 'elevator', 'furnished', 'swimming_pool', 'new', 'district_zone']], aspect=1)
Out[82]:
<seaborn.axisgrid.PairGrid at 0x7f121a09a8c0>

Feature Scaling¶

Comentário: optou-se pela não realização do feature scaling (normalização) principalmente pela sua potencial perda de interpretabildiade. Os valores normalizados podem não ter significado direto, dificultando sua a interpretação. Além disso, em relação aos modelos aplicados como XGBoosting e LightGBM há referências que informam que sua performance não é afetada pela normalização dos dados.

🤖 3.Modelagem Estatística¶

In [83]:
# Importando todas as bibliotecas para o uso dos modelos

import xgboost as xgb
import optuna
import lightgbm as lgb

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import cross_val_score
In [84]:
# Transformando a coluna District_Zone em dummies

treino_base_venda = pd.get_dummies(treino_base_venda, columns=['district_zone'])
teste_base_venda = pd.get_dummies(teste_base_venda, columns=['district_zone'])

treino_base_aluguel = pd.get_dummies(treino_base_aluguel, columns=['district_zone'])
teste_base_aluguel = pd.get_dummies(teste_base_aluguel, columns=['district_zone'])
In [85]:
treino_base_venda.columns
Out[85]:
Index(['price', 'elevator', 'furnished', 'size', 'toilets', 'property_type',
       'condo', 'swimming_pool', 'rooms', 'negotiation_type', 'parking',
       'suites', 'new', 'district_zone_Centro', 'district_zone_Leste',
       'district_zone_Norte', 'district_zone_Oeste', 'district_zone_Sul'],
      dtype='object')
In [86]:
# Listando as variáveis explicativas

vars_exp = ['condo', 'size', 'rooms', 'toilets', 'suites', 'parking', 'elevator', 'furnished', 'swimming_pool', 'new', 'district_zone_Centro', 'district_zone_Leste', 'district_zone_Norte', 'district_zone_Oeste']

# Listando a variável resposta

vars_resp = ['price']

🤖 3.1 Modelagem Estatística - Regressor Linear - Venda¶

In [87]:
regressor_venda = LinearRegression()

# Treine o regressor com os dados de treinamento
regressor_venda.fit(treino_base_venda[vars_exp], treino_base_venda[vars_resp])

#CONJUNTO TREINO#

y_pred_treino = regressor_venda.predict(treino_base_venda[vars_exp])

# Calcule o MSE (Mean Squared Error)
mse_treino = mean_squared_error(treino_base_venda[vars_resp], y_pred_treino)

# Calcule o RMSE (Root Mean Squared Error)

rmse_treino = np.sqrt(mse_treino)

# Calcule R²

r2_treino = r2_score(treino_base_venda[vars_resp], y_pred_treino)


print(f'''Treino - Mean Squared Error (MSE): {mse_treino}''')
print(f'''Treino - Root Mean Squared Error (RMSE): {rmse_treino}''')
print(f'''Treino - R²: {r2_treino}\n''')

#CONJUNTO TESTE#

# Faça previsões com base nos dados de teste
y_pred = regressor_venda.predict(teste_base_venda[vars_exp])

# Calcule o MSE (Mean Squared Error)
mse = mean_squared_error(teste_base_venda[vars_resp], y_pred)

# Calcule o RMSE (Root Mean Squared Error)

rmse = np.sqrt(mse)

# Calcule R²

r2 = r2_score(teste_base_venda[vars_resp], y_pred)

print(f'''Teste - Mean Squared Error (MSE): {mse}''')
print(f'''Teste - Root Mean Squared Error (RMSE): {rmse}''')
print(f'''Teste - R²: {r2}''')

Treino - Mean Squared Error (MSE): 138680013505.34702
Treino - Root Mean Squared Error (RMSE): 372397.65507498436
Treino - R²: 0.7478980952893147

Teste - Mean Squared Error (MSE): 157884879195.57742
Teste - Root Mean Squared Error (RMSE): 397347.3029927061
Teste - R²: 0.7246454428633121
In [88]:
# Verificando os coeficientes de cada variável e o intercepto

coeficientes = regressor_venda.coef_

df_venda_coef = pd.DataFrame({'Variável': vars_exp, 'Coeficiente': coeficientes[0]})
df_venda_coef.sort_values(by='Coeficiente', ascending=False)
Out[88]:
Variável Coeficiente
13 district_zone_Oeste 210917.294642
5 parking 207334.936199
4 suites 119685.011114
9 new 86791.286133
8 swimming_pool 47475.615299
10 district_zone_Centro 34416.038207
1 size 9276.295957
0 condo 68.729550
3 toilets -17727.056128
7 furnished -20352.439673
6 elevator -35097.569027
11 district_zone_Leste -36700.824884
12 district_zone_Norte -98563.223895
2 rooms -144446.439475
In [89]:
print(f'''O intercepto é {regressor_venda.intercept_}''')

O intercepto é [-190180.9667955]

Comentário: Note que para venda as variáveis que contribuem positivamente para o valor do preço são District_Zone_Oeste, Parking, Suites, New, Swimming Pool, District_Zone_Centro e Size. A variável de Condomínio ficou próximo de zero, contribuindo pouco para o valor final. Já as variáveis que contribuem negativamente são Furnished, Elevator, Toilets, District_Zone_Leste, District_Zone_Norte e Rooms.

In [90]:
# Gráfico de dispersão

GraficoDispersao(teste_base_venda[vars_resp], y_pred, 'Gráfico de Dispersão entre Valores Reais e Previstos', 'Valores Reais', 'Valores Previstos')

Cross-validation¶

In [91]:
lin_reg = LinearRegression()
lin_scores = cross_val_score(lin_reg, treino_base_venda[vars_exp], treino_base_venda[vars_resp], scoring="neg_mean_squared_error", cv=10)

lin_rmse_scores = np.sqrt(-lin_scores)

display_scores(lin_rmse_scores)

Scores: [302448.43890804 300780.79595946 302883.57080222 378605.89970777
 352403.21298198 285478.33642867 472450.49145668 625974.45052617
 324318.53490436 348950.6211116 ]
Mean: 369429.43527869426
Standard deviation: 99966.18748556009

🤖 3.2 Modelagem Estatística - Regressor Linear - Aluguel¶

In [92]:
regressor_aluguel = LinearRegression()

# Treine o regressor com os dados de treinamento
regressor_aluguel.fit(treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp])

#CONJUNTO TREINO#

y_pred_treino = regressor_aluguel.predict(treino_base_aluguel[vars_exp])

# Calcule o MSE (Mean Squared Error)
mse_treino = mean_squared_error(treino_base_aluguel[vars_resp], y_pred_treino)

# Calcule o RMSE (Root Mean Squared Error)

rmse_treino = np.sqrt(mse_treino)

# Calcule R²

r2_treino = r2_score(treino_base_aluguel[vars_resp], y_pred_treino)

print(f'''Treino - Mean Squared Error (MSE): {mse_treino}''')
print(f'''Treino - Root Mean Squared Error (RMSE): {rmse_treino}''')
print(f'''Treino - R²: {r2_treino}\n''')

#CONJUNTO TESTE#

# Faça previsões com base nos dados de teste
y_pred = regressor_aluguel.predict(teste_base_aluguel[vars_exp])

# Calcule o MSE (Mean Squared Error)
mse = mean_squared_error(teste_base_aluguel[vars_resp], y_pred)

# Calcule o RMSE (Root Mean Squared Error)

rmse = np.sqrt(mse)

# Calcule R²

r2 = r2_score(teste_base_aluguel[vars_resp], y_pred)

print(f'''Teste - Mean Squared Error (MSE): {mse}''')
print(f'''Teste - Root Mean Squared Error (RMSE): {rmse}''')
print(f'''Teste - R²: {r2}''')

Treino - Mean Squared Error (MSE): 4975451.895782311
Treino - Root Mean Squared Error (RMSE): 2230.5721005567857
Treino - R²: 0.6200706385290691

Teste - Mean Squared Error (MSE): 3938415.669971025
Teste - Root Mean Squared Error (RMSE): 1984.5441970314052
Teste - R²: 0.6374033558600395
In [93]:
# Verificando os coeficientes de cada variável e o intercepto

coeficientes = regressor_aluguel.coef_

df_aluguel_coef = pd.DataFrame({'Variável': vars_exp, 'Coeficiente': coeficientes[0]})
df_aluguel_coef.sort_values(by='Coeficiente', ascending=False)
Out[93]:
Variável Coeficiente
7 furnished 1004.086391
5 parking 450.939325
3 toilets 234.313322
8 swimming_pool 221.517846
13 district_zone_Oeste 187.274534
4 suites 153.145817
1 size 30.166592
0 condo 0.753343
9 new -8.242685
6 elevator -95.039852
10 district_zone_Centro -245.970129
11 district_zone_Leste -365.868994
12 district_zone_Norte -699.195138
2 rooms -802.886821
In [94]:
print(f'''O intercepto é {regressor_aluguel.intercept_}''')

O intercepto é [244.86255226]

Comentário: Note que para aluguel as variáveis que contribuem positivamente para o valor do preço são Furnished, Parking, Toilets, Swimming Pool, New, District_Zone_Oeste, Suites e Size. A variável de Condomínio também ficou próximo de zero, contribuindo pouco para o valor final. Já as variáveis que contribuem negativamente são Elevator, District_Zone_Centro, District_Zone_Leste, District_Zone_Norte e Rooms. Também note que as variáveis que mais contribuiram negativamente para o preço do aluguel foram variáveis de local

In [95]:
# Gráfico de dispersão

GraficoDispersao(teste_base_aluguel[vars_resp], y_pred, 'Gráfico de Dispersão entre Valores Reais e Previstos', 'Valores Reais', 'Valores Previstos')

Cross-validation¶

In [96]:
lin_reg = LinearRegression()
lin_scores = cross_val_score(lin_reg, treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp], scoring="neg_mean_squared_error", cv=10)

lin_rmse_scores = np.sqrt(-lin_scores)

display_scores(lin_rmse_scores)

Scores: [1641.64743747 1572.76509725 1755.59014378 1306.5021472  1229.15545513
 3357.39228116 2820.84596249 2560.29361744 2482.91369719 2719.95488887]
Mean: 2144.706072797478
Standard deviation: 694.1322659229307

🤖 3.3 Modelagem Estatística - XGBoost - Venda¶

In [97]:
optuna.logging.set_verbosity(optuna.logging.ERROR)
optuna.logging.disable_default_handler()
optuna.logging.disable_propagation()
In [98]:
# Função de otimização do Optuna


def objective(trial):
    param = {
        'objective': 'reg:squarederror',
        'eval_metric': 'rmse',
        'booster': 'gbtree',
        'lambda': trial.suggest_float('lambda', 1e-8, 1.0),
        'alpha': trial.suggest_float('alpha', 1e-8, 1.0),
        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),
        'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
        'max_depth': trial.suggest_int('max_depth', 3, 10),
        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),
        'subsample': trial.suggest_float('subsample', 0.6, 0.9, step=0.1),
        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 0.9, step=0.1),
        'random_state': 42,
    }

    model = xgb.XGBRegressor(**param)
    model.fit(treino_base_venda[vars_exp], treino_base_venda[vars_resp])
    y_pred = model.predict(teste_base_venda[vars_exp])
    rmse = np.sqrt(mean_squared_error(teste_base_venda[vars_resp], y_pred))
    return rmse

# Estudo do Optuna
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=100)
In [99]:
# Imprimindo os melhores parâmetros do XGBRegressor segundo o Optuna
best_params = study.best_params
print("Melhores Parâmetros:")
print(best_params)

Melhores Parâmetros:
{'lambda': 0.9413823312175587, 'alpha': 0.5608529672529157, 'learning_rate': 0.09334405447148263, 'n_estimators': 704, 'max_depth': 10, 'min_child_weight': 1, 'subsample': 0.8, 'colsample_bytree': 0.8}
In [100]:
# Criando um modelo final com os melhores parâmetros
final_model = xgb.XGBRegressor(**best_params)
final_model.fit(treino_base_venda[vars_exp], treino_base_venda[vars_resp])

#CONJUNTO DE TREINO#

y_pred_final_treino = final_model.predict(treino_base_venda[vars_exp])

rmse_final_treino = np.sqrt(mean_squared_error(treino_base_venda[vars_resp], y_pred_final_treino))

print(f"Treino - RMSE do Modelo Final: {rmse_final_treino}")

#CONJUNTO DE TESTE

# Previsões com o modelo final
y_pred_final = final_model.predict(teste_base_venda[vars_exp])

# Calculando a métrica RMSE com o modelo final
rmse_final = np.sqrt(mean_squared_error(teste_base_venda[vars_resp], y_pred_final))

print(f"Teste - RMSE do Modelo Final: {rmse_final}")

Treino - RMSE do Modelo Final: 49808.14309097193
Teste - RMSE do Modelo Final: 104509.97137774626
In [101]:
# Verificando a importância relativa de cada variável

importancias_variaveis = final_model.feature_importances_

df_venda_xbg = pd.DataFrame({'Variável': vars_exp, 'Coeficiente': importancias_variaveis})
df_venda_xbg.sort_values(by='Coeficiente', ascending=False)
Out[101]:
Variável Coeficiente
5 parking 0.324954
1 size 0.214045
13 district_zone_Oeste 0.133000
12 district_zone_Norte 0.074811
4 suites 0.049934
3 toilets 0.041904
11 district_zone_Leste 0.035889
0 condo 0.032178
7 furnished 0.019108
8 swimming_pool 0.017342
10 district_zone_Centro 0.017246
2 rooms 0.015168
6 elevator 0.013193
9 new 0.011229

Comentários: Note que mais de 50% da importância das variáveis para este modelo de venda foi referente a vagas de estacionamento e tamanho da propriedade. Já as variáveis que menos tiveram importância (menos de 2%) foram relacionados a mobilia, existência ou não de piscina e elevadores e se a propriedade era nova.

In [102]:
# Gráfico de dispersão

GraficoDispersao(teste_base_venda[vars_resp], y_pred_final, 'Gráfico de Dispersão entre Valores Reais e Previstos', 'Valores Reais', 'Valores Previstos')

Cross-validation¶

In [103]:
xgb_reg = xgb.XGBRegressor()
xgb_scores = cross_val_score(xgb_reg, treino_base_venda[vars_exp], treino_base_venda[vars_resp], scoring="neg_mean_squared_error", cv=10)

xgb_rmse_scores = np.sqrt(-xgb_scores)

display_scores(xgb_rmse_scores)

Scores: [223026.47453002 261717.11240228  96029.88510312 154926.33842362
 163757.48154861 153726.62430038 261684.81577922 420245.72411576
 263721.96835937 274487.46363774]
Mean: 227332.38882001172
Standard deviation: 86652.67329295211

🤖 3.4 Modelagem Estatística - XGBoost - Aluguel¶

In [104]:
# Função de otimização do Optuna
def objective(trial):
    param = {
        'objective': 'reg:squarederror',
        'eval_metric': 'rmse',
        'booster': 'gbtree',
        'lambda': trial.suggest_float('lambda', 1e-8, 1.0),
        'alpha': trial.suggest_float('alpha', 1e-8, 1.0),
        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),
        'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
        'max_depth': trial.suggest_int('max_depth', 3, 10),
        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),
        'subsample': trial.suggest_float('subsample', 0.6, 0.9, step=0.1),
        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 0.9, step=0.1),
        'random_state': 42,
    }

    model = xgb.XGBRegressor(**param)
    model.fit(treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp])
    y_pred = model.predict(teste_base_aluguel[vars_exp])
    rmse = np.sqrt(mean_squared_error(teste_base_aluguel[vars_resp], y_pred))
    return rmse

# Estudo do Optuna
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=100)
In [105]:
# Imprimindo os melhores parâmetros do XGBRegressor segundo o Optuna
best_params = study.best_params
print("Melhores Parâmetros:")
print(best_params)

Melhores Parâmetros:
{'lambda': 0.4854991772217221, 'alpha': 0.4830269871566104, 'learning_rate': 0.2926351503305343, 'n_estimators': 681, 'max_depth': 9, 'min_child_weight': 1, 'subsample': 0.9, 'colsample_bytree': 0.8}
In [106]:
# Criando o modelo final com os melhores parâmetros
final_model = xgb.XGBRegressor(**best_params)
final_model.fit(treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp])

#CONJUNTO DE TREINO

y_pred_final_treino = final_model.predict(treino_base_aluguel[vars_exp])

rmse_final_treino = np.sqrt(mean_squared_error(treino_base_aluguel[vars_resp], y_pred_final_treino))

print(f"Treino - RMSE do Modelo Final: {rmse_final_treino}")

# Previsões com o modelo final
y_pred_final = final_model.predict(teste_base_aluguel[vars_exp])

# Calculando a métrica RMSE com o modelo final
rmse_final = np.sqrt(mean_squared_error(teste_base_aluguel[vars_resp], y_pred_final))

print(f"Teste - RMSE do Modelo Final: {rmse_final}")

Treino - RMSE do Modelo Final: 178.98137712125177
Teste - RMSE do Modelo Final: 924.6115086592067
In [107]:
# Verificando a importância relativa de cada variável

importancias_variaveis = final_model.feature_importances_

df_aluguel_xbg = pd.DataFrame({'Variável': vars_exp, 'Coeficiente': importancias_variaveis})
df_aluguel_xbg.sort_values(by='Coeficiente', ascending=False)
Out[107]:
Variável Coeficiente
1 size 0.219341
4 suites 0.162252
0 condo 0.095632
5 parking 0.074942
7 furnished 0.068335
13 district_zone_Oeste 0.065340
3 toilets 0.061595
11 district_zone_Leste 0.058509
10 district_zone_Centro 0.049976
8 swimming_pool 0.044754
12 district_zone_Norte 0.043198
2 rooms 0.031731
6 elevator 0.022764
9 new 0.001630

Comentários: Quase 70% da importância ficou em quatro variáveis: número de quartos com banheiro privativo, número de vagas de estacionamento, tamanho da propriedade e que a propriedade estava mobiliada ou não. Perceba que a variável se o imóvel era novo tenho 0% de relevância

In [108]:
# Gráfico de dispersão

GraficoDispersao(teste_base_aluguel[vars_resp], y_pred_final, 'Gráfico de Dispersão entre Valores Reais e Previstos', 'Valores Reais', 'Valores Previstos')

Cross-validation¶

In [109]:
xgb_reg = xgb.XGBRegressor()
xgb_scores = cross_val_score(xgb_reg, treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp], scoring="neg_mean_squared_error", cv=10)

xgb_rmse_scores = np.sqrt(-xgb_scores)

display_scores(xgb_rmse_scores)

Scores: [2941.30236308 1372.59611907 1059.69738291  581.67203703  752.54277676
 2611.46548529 2397.23564968 1509.69906453 1708.98758869 2370.31262522]
Mean: 1730.5511092253776
Standard deviation: 774.5664813021807

🤖 3.5 Modelagem Estatística - LightGBM - Venda¶

In [110]:
# Função de otimização do Optuna
def objective(trial):
    param = {
        'objective': 'regression',
        'metric': 'rmse',
        'boosting_type': 'gbdt',
        'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
        'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
        'num_leaves': trial.suggest_int('num_leaves', 2, 256),
        'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
        'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
        'bagging_freq': trial.suggest_int('bagging_freq', 1, 10),
        'min_child_samples': trial.suggest_int('min_child_samples', 1, 100),
        'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),
        'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
        'random_state': 42,
    }

    model = lgb.LGBMRegressor(**param)
    model.fit(treino_base_venda[vars_exp], treino_base_venda[vars_resp])
    y_pred = model.predict(teste_base_venda[vars_exp])
    rmse = np.sqrt(mean_squared_error(teste_base_venda[vars_resp], y_pred))
    return rmse

# Estudo do Optuna
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=100)

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.44834835436621945, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.44834835436621945
[LightGBM] [Warning] lambda_l2 is set=0.7901764089363529, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.7901764089363529
[LightGBM] [Warning] lambda_l1 is set=1.050006192453889, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.050006192453889
[LightGBM] [Warning] bagging_fraction is set=0.9956331964848976, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9956331964848976
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.44834835436621945, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.44834835436621945
[LightGBM] [Warning] lambda_l2 is set=0.7901764089363529, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.7901764089363529
[LightGBM] [Warning] lambda_l1 is set=1.050006192453889, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.050006192453889
[LightGBM] [Warning] bagging_fraction is set=0.9956331964848976, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9956331964848976
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.44834835436621945, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.44834835436621945
[LightGBM] [Warning] lambda_l2 is set=0.7901764089363529, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.7901764089363529
[LightGBM] [Warning] lambda_l1 is set=1.050006192453889, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.050006192453889
[LightGBM] [Warning] bagging_fraction is set=0.9956331964848976, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9956331964848976
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9088357641555205, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9088357641555205
[LightGBM] [Warning] lambda_l2 is set=8.926830415843893, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.926830415843893
[LightGBM] [Warning] lambda_l1 is set=2.076073195150787e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.076073195150787e-08
[LightGBM] [Warning] bagging_fraction is set=0.973608666861349, subsample=1.0 will be ignored. Current value: bagging_fraction=0.973608666861349
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9088357641555205, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9088357641555205
[LightGBM] [Warning] lambda_l2 is set=8.926830415843893, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.926830415843893
[LightGBM] [Warning] lambda_l1 is set=2.076073195150787e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.076073195150787e-08
[LightGBM] [Warning] bagging_fraction is set=0.973608666861349, subsample=1.0 will be ignored. Current value: bagging_fraction=0.973608666861349
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9088357641555205, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9088357641555205
[LightGBM] [Warning] lambda_l2 is set=8.926830415843893, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.926830415843893
[LightGBM] [Warning] lambda_l1 is set=2.076073195150787e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.076073195150787e-08
[LightGBM] [Warning] bagging_fraction is set=0.973608666861349, subsample=1.0 will be ignored. Current value: bagging_fraction=0.973608666861349
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.5894004853225578, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5894004853225578
[LightGBM] [Warning] lambda_l2 is set=2.1221879278169038e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1221879278169038e-07
[LightGBM] [Warning] lambda_l1 is set=7.102881763921396e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.102881763921396e-07
[LightGBM] [Warning] bagging_fraction is set=0.3940000943038565, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3940000943038565
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.5894004853225578, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5894004853225578
[LightGBM] [Warning] lambda_l2 is set=2.1221879278169038e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1221879278169038e-07
[LightGBM] [Warning] lambda_l1 is set=7.102881763921396e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.102881763921396e-07
[LightGBM] [Warning] bagging_fraction is set=0.3940000943038565, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3940000943038565
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001046 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.5894004853225578, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5894004853225578
[LightGBM] [Warning] lambda_l2 is set=2.1221879278169038e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1221879278169038e-07
[LightGBM] [Warning] lambda_l1 is set=7.102881763921396e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.102881763921396e-07
[LightGBM] [Warning] bagging_fraction is set=0.3940000943038565, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3940000943038565
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7838820777081038, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7838820777081038
[LightGBM] [Warning] lambda_l2 is set=0.00010718512923991979, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00010718512923991979
[LightGBM] [Warning] lambda_l1 is set=2.0214080155756683e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0214080155756683e-07
[LightGBM] [Warning] bagging_fraction is set=0.6315213222644723, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6315213222644723
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7838820777081038, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7838820777081038
[LightGBM] [Warning] lambda_l2 is set=0.00010718512923991979, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00010718512923991979
[LightGBM] [Warning] lambda_l1 is set=2.0214080155756683e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0214080155756683e-07
[LightGBM] [Warning] bagging_fraction is set=0.6315213222644723, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6315213222644723
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7838820777081038, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7838820777081038
[LightGBM] [Warning] lambda_l2 is set=0.00010718512923991979, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00010718512923991979
[LightGBM] [Warning] lambda_l1 is set=2.0214080155756683e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0214080155756683e-07
[LightGBM] [Warning] bagging_fraction is set=0.6315213222644723, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6315213222644723
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.706919242833237, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.706919242833237
[LightGBM] [Warning] lambda_l2 is set=4.5629262152378974e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.5629262152378974e-05
[LightGBM] [Warning] lambda_l1 is set=0.006939049409666816, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.006939049409666816
[LightGBM] [Warning] bagging_fraction is set=0.15576895584009623, subsample=1.0 will be ignored. Current value: bagging_fraction=0.15576895584009623
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.706919242833237, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.706919242833237
[LightGBM] [Warning] lambda_l2 is set=4.5629262152378974e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.5629262152378974e-05
[LightGBM] [Warning] lambda_l1 is set=0.006939049409666816, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.006939049409666816
[LightGBM] [Warning] bagging_fraction is set=0.15576895584009623, subsample=1.0 will be ignored. Current value: bagging_fraction=0.15576895584009623
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),
/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.706919242833237, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.706919242833237
[LightGBM] [Warning] lambda_l2 is set=4.5629262152378974e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.5629262152378974e-05
[LightGBM] [Warning] lambda_l1 is set=0.006939049409666816, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.006939049409666816
[LightGBM] [Warning] bagging_fraction is set=0.15576895584009623, subsample=1.0 will be ignored. Current value: bagging_fraction=0.15576895584009623
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.6315094071636311, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6315094071636311
[LightGBM] [Warning] lambda_l2 is set=0.8293222954545298, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.8293222954545298
[LightGBM] [Warning] lambda_l1 is set=2.509620753882286e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.509620753882286e-07
[LightGBM] [Warning] bagging_fraction is set=0.19959959142104705, subsample=1.0 will be ignored. Current value: bagging_fraction=0.19959959142104705
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.6315094071636311, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6315094071636311
[LightGBM] [Warning] lambda_l2 is set=0.8293222954545298, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.8293222954545298
[LightGBM] [Warning] lambda_l1 is set=2.509620753882286e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.509620753882286e-07
[LightGBM] [Warning] bagging_fraction is set=0.19959959142104705, subsample=1.0 will be ignored. Current value: bagging_fraction=0.19959959142104705
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001028 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.6315094071636311, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6315094071636311
[LightGBM] [Warning] lambda_l2 is set=0.8293222954545298, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.8293222954545298
[LightGBM] [Warning] lambda_l1 is set=2.509620753882286e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.509620753882286e-07
[LightGBM] [Warning] bagging_fraction is set=0.19959959142104705, subsample=1.0 will be ignored. Current value: bagging_fraction=0.19959959142104705
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.5907705861323204, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5907705861323204
[LightGBM] [Warning] lambda_l2 is set=7.843664263657888e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.843664263657888e-08
[LightGBM] [Warning] lambda_l1 is set=2.0392092572014797, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0392092572014797
[LightGBM] [Warning] bagging_fraction is set=0.37907169176224853, subsample=1.0 will be ignored. Current value: bagging_fraction=0.37907169176224853
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.5907705861323204, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5907705861323204
[LightGBM] [Warning] lambda_l2 is set=7.843664263657888e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.843664263657888e-08
[LightGBM] [Warning] lambda_l1 is set=2.0392092572014797, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0392092572014797
[LightGBM] [Warning] bagging_fraction is set=0.37907169176224853, subsample=1.0 will be ignored. Current value: bagging_fraction=0.37907169176224853
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001056 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.5907705861323204, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5907705861323204
[LightGBM] [Warning] lambda_l2 is set=7.843664263657888e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.843664263657888e-08
[LightGBM] [Warning] lambda_l1 is set=2.0392092572014797, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0392092572014797
[LightGBM] [Warning] bagging_fraction is set=0.37907169176224853, subsample=1.0 will be ignored. Current value: bagging_fraction=0.37907169176224853

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),
/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.24613556743401782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.24613556743401782
[LightGBM] [Warning] lambda_l2 is set=3.312715012747482e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.312715012747482e-05
[LightGBM] [Warning] lambda_l1 is set=0.01391679074533519, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.01391679074533519
[LightGBM] [Warning] bagging_fraction is set=0.5711659491916513, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5711659491916513
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.24613556743401782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.24613556743401782
[LightGBM] [Warning] lambda_l2 is set=3.312715012747482e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.312715012747482e-05
[LightGBM] [Warning] lambda_l1 is set=0.01391679074533519, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.01391679074533519
[LightGBM] [Warning] bagging_fraction is set=0.5711659491916513, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5711659491916513
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.24613556743401782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.24613556743401782
[LightGBM] [Warning] lambda_l2 is set=3.312715012747482e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.312715012747482e-05
[LightGBM] [Warning] lambda_l1 is set=0.01391679074533519, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.01391679074533519
[LightGBM] [Warning] bagging_fraction is set=0.5711659491916513, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5711659491916513
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7166718811483851, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7166718811483851
[LightGBM] [Warning] lambda_l2 is set=1.98176749647388e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.98176749647388e-07
[LightGBM] [Warning] lambda_l1 is set=0.018058293200316423, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.018058293200316423
[LightGBM] [Warning] bagging_fraction is set=0.5729217843611127, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5729217843611127
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7166718811483851, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7166718811483851
[LightGBM] [Warning] lambda_l2 is set=1.98176749647388e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.98176749647388e-07
[LightGBM] [Warning] lambda_l1 is set=0.018058293200316423, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.018058293200316423
[LightGBM] [Warning] bagging_fraction is set=0.5729217843611127, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5729217843611127
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7166718811483851, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7166718811483851
[LightGBM] [Warning] lambda_l2 is set=1.98176749647388e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.98176749647388e-07
[LightGBM] [Warning] lambda_l1 is set=0.018058293200316423, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.018058293200316423
[LightGBM] [Warning] bagging_fraction is set=0.5729217843611127, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5729217843611127
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.5546726345271072, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5546726345271072
[LightGBM] [Warning] lambda_l2 is set=0.0006551218718592712, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0006551218718592712
[LightGBM] [Warning] lambda_l1 is set=1.0720098859546954, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0720098859546954
[LightGBM] [Warning] bagging_fraction is set=0.49455252827012475, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49455252827012475
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.5546726345271072, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5546726345271072
[LightGBM] [Warning] lambda_l2 is set=0.0006551218718592712, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0006551218718592712
[LightGBM] [Warning] lambda_l1 is set=1.0720098859546954, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0720098859546954
[LightGBM] [Warning] bagging_fraction is set=0.49455252827012475, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49455252827012475
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.5546726345271072, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5546726345271072
[LightGBM] [Warning] lambda_l2 is set=0.0006551218718592712, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0006551218718592712
[LightGBM] [Warning] lambda_l1 is set=1.0720098859546954, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0720098859546954
[LightGBM] [Warning] bagging_fraction is set=0.49455252827012475, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49455252827012475
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9912497555594494, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9912497555594494
[LightGBM] [Warning] lambda_l2 is set=9.431594741262025, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.431594741262025
[LightGBM] [Warning] lambda_l1 is set=1.144329613479112e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.144329613479112e-05
[LightGBM] [Warning] bagging_fraction is set=0.9764054642966955, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9764054642966955
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9912497555594494, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9912497555594494
[LightGBM] [Warning] lambda_l2 is set=9.431594741262025, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.431594741262025
[LightGBM] [Warning] lambda_l1 is set=1.144329613479112e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.144329613479112e-05
[LightGBM] [Warning] bagging_fraction is set=0.9764054642966955, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9764054642966955
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9912497555594494, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9912497555594494
[LightGBM] [Warning] lambda_l2 is set=9.431594741262025, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.431594741262025
[LightGBM] [Warning] lambda_l1 is set=1.144329613479112e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.144329613479112e-05
[LightGBM] [Warning] bagging_fraction is set=0.9764054642966955, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9764054642966955
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9350144478880132, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9350144478880132
[LightGBM] [Warning] lambda_l2 is set=0.009312670092653162, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.009312670092653162
[LightGBM] [Warning] lambda_l1 is set=5.9398147313567726e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.9398147313567726e-05
[LightGBM] [Warning] bagging_fraction is set=0.7843248842745562, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7843248842745562
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9350144478880132, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9350144478880132
[LightGBM] [Warning] lambda_l2 is set=0.009312670092653162, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.009312670092653162
[LightGBM] [Warning] lambda_l1 is set=5.9398147313567726e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.9398147313567726e-05
[LightGBM] [Warning] bagging_fraction is set=0.7843248842745562, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7843248842745562
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9350144478880132, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9350144478880132
[LightGBM] [Warning] lambda_l2 is set=0.009312670092653162, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.009312670092653162
[LightGBM] [Warning] lambda_l1 is set=5.9398147313567726e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.9398147313567726e-05
[LightGBM] [Warning] bagging_fraction is set=0.7843248842745562, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7843248842745562

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9918735387357461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9918735387357461
[LightGBM] [Warning] lambda_l2 is set=0.031883237986539324, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.031883237986539324
[LightGBM] [Warning] lambda_l1 is set=1.1269126911260117e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1269126911260117e-08
[LightGBM] [Warning] bagging_fraction is set=0.8152316643849297, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8152316643849297
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9918735387357461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9918735387357461
[LightGBM] [Warning] lambda_l2 is set=0.031883237986539324, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.031883237986539324
[LightGBM] [Warning] lambda_l1 is set=1.1269126911260117e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1269126911260117e-08
[LightGBM] [Warning] bagging_fraction is set=0.8152316643849297, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8152316643849297
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9918735387357461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9918735387357461
[LightGBM] [Warning] lambda_l2 is set=0.031883237986539324, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.031883237986539324
[LightGBM] [Warning] lambda_l1 is set=1.1269126911260117e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1269126911260117e-08
[LightGBM] [Warning] bagging_fraction is set=0.8152316643849297, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8152316643849297

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8805404135704443, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8805404135704443
[LightGBM] [Warning] lambda_l2 is set=0.006360529843639432, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.006360529843639432
[LightGBM] [Warning] lambda_l1 is set=3.44888591205206e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.44888591205206e-05
[LightGBM] [Warning] bagging_fraction is set=0.7481874567480223, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7481874567480223
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8805404135704443, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8805404135704443
[LightGBM] [Warning] lambda_l2 is set=0.006360529843639432, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.006360529843639432
[LightGBM] [Warning] lambda_l1 is set=3.44888591205206e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.44888591205206e-05
[LightGBM] [Warning] bagging_fraction is set=0.7481874567480223, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7481874567480223
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8805404135704443, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8805404135704443
[LightGBM] [Warning] lambda_l2 is set=0.006360529843639432, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.006360529843639432
[LightGBM] [Warning] lambda_l1 is set=3.44888591205206e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.44888591205206e-05
[LightGBM] [Warning] bagging_fraction is set=0.7481874567480223, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7481874567480223

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8708074739450257, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8708074739450257
[LightGBM] [Warning] lambda_l2 is set=0.014414712390071254, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.014414712390071254
[LightGBM] [Warning] lambda_l1 is set=5.068329033896503e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.068329033896503e-06
[LightGBM] [Warning] bagging_fraction is set=0.7666594901948615, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7666594901948615
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8708074739450257, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8708074739450257
[LightGBM] [Warning] lambda_l2 is set=0.014414712390071254, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.014414712390071254
[LightGBM] [Warning] lambda_l1 is set=5.068329033896503e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.068329033896503e-06
[LightGBM] [Warning] bagging_fraction is set=0.7666594901948615, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7666594901948615
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8708074739450257, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8708074739450257
[LightGBM] [Warning] lambda_l2 is set=0.014414712390071254, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.014414712390071254
[LightGBM] [Warning] lambda_l1 is set=5.068329033896503e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.068329033896503e-06
[LightGBM] [Warning] bagging_fraction is set=0.7666594901948615, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7666594901948615

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.9855687862289249, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9855687862289249
[LightGBM] [Warning] lambda_l2 is set=0.01584746078705404, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.01584746078705404
[LightGBM] [Warning] lambda_l1 is set=2.0724521733538713e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0724521733538713e-08
[LightGBM] [Warning] bagging_fraction is set=0.7699533894684193, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7699533894684193
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.9855687862289249, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9855687862289249
[LightGBM] [Warning] lambda_l2 is set=0.01584746078705404, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.01584746078705404
[LightGBM] [Warning] lambda_l1 is set=2.0724521733538713e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0724521733538713e-08
[LightGBM] [Warning] bagging_fraction is set=0.7699533894684193, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7699533894684193
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.9855687862289249, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9855687862289249
[LightGBM] [Warning] lambda_l2 is set=0.01584746078705404, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.01584746078705404
[LightGBM] [Warning] lambda_l1 is set=2.0724521733538713e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.0724521733538713e-08
[LightGBM] [Warning] bagging_fraction is set=0.7699533894684193, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7699533894684193

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8252290491632323, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8252290491632323
[LightGBM] [Warning] lambda_l2 is set=0.00208972317075515, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00208972317075515
[LightGBM] [Warning] lambda_l1 is set=0.0002831081506217994, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002831081506217994
[LightGBM] [Warning] bagging_fraction is set=0.8496703741443526, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8496703741443526
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8252290491632323, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8252290491632323
[LightGBM] [Warning] lambda_l2 is set=0.00208972317075515, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00208972317075515
[LightGBM] [Warning] lambda_l1 is set=0.0002831081506217994, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002831081506217994
[LightGBM] [Warning] bagging_fraction is set=0.8496703741443526, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8496703741443526
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8252290491632323, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8252290491632323
[LightGBM] [Warning] lambda_l2 is set=0.00208972317075515, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00208972317075515
[LightGBM] [Warning] lambda_l1 is set=0.0002831081506217994, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002831081506217994
[LightGBM] [Warning] bagging_fraction is set=0.8496703741443526, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8496703741443526

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8137644618412456, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8137644618412456
[LightGBM] [Warning] lambda_l2 is set=6.356098685263748e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.356098685263748e-06
[LightGBM] [Warning] lambda_l1 is set=0.0006769522646102742, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0006769522646102742
[LightGBM] [Warning] bagging_fraction is set=0.8771256639795267, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8771256639795267
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8137644618412456, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8137644618412456
[LightGBM] [Warning] lambda_l2 is set=6.356098685263748e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.356098685263748e-06
[LightGBM] [Warning] lambda_l1 is set=0.0006769522646102742, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0006769522646102742
[LightGBM] [Warning] bagging_fraction is set=0.8771256639795267, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8771256639795267
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8137644618412456, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8137644618412456
[LightGBM] [Warning] lambda_l2 is set=6.356098685263748e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.356098685263748e-06
[LightGBM] [Warning] lambda_l1 is set=0.0006769522646102742, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0006769522646102742
[LightGBM] [Warning] bagging_fraction is set=0.8771256639795267, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8771256639795267
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8184831714632798, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8184831714632798
[LightGBM] [Warning] lambda_l2 is set=1.7954826729090214e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7954826729090214e-06
[LightGBM] [Warning] lambda_l1 is set=0.0006246340682392365, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0006246340682392365
[LightGBM] [Warning] bagging_fraction is set=0.8582948433308111, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8582948433308111
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8184831714632798, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8184831714632798
[LightGBM] [Warning] lambda_l2 is set=1.7954826729090214e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7954826729090214e-06
[LightGBM] [Warning] lambda_l1 is set=0.0006246340682392365, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0006246340682392365
[LightGBM] [Warning] bagging_fraction is set=0.8582948433308111, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8582948433308111
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8184831714632798, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8184831714632798
[LightGBM] [Warning] lambda_l2 is set=1.7954826729090214e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7954826729090214e-06
[LightGBM] [Warning] lambda_l1 is set=0.0006246340682392365, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0006246340682392365
[LightGBM] [Warning] bagging_fraction is set=0.8582948433308111, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8582948433308111
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7955804401422456, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7955804401422456
[LightGBM] [Warning] lambda_l2 is set=8.406074577518491e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.406074577518491e-06
[LightGBM] [Warning] lambda_l1 is set=0.00034706522458153666, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00034706522458153666
[LightGBM] [Warning] bagging_fraction is set=0.8916392534530743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8916392534530743
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7955804401422456, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7955804401422456
[LightGBM] [Warning] lambda_l2 is set=8.406074577518491e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.406074577518491e-06
[LightGBM] [Warning] lambda_l1 is set=0.00034706522458153666, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00034706522458153666
[LightGBM] [Warning] bagging_fraction is set=0.8916392534530743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8916392534530743
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7955804401422456, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7955804401422456
[LightGBM] [Warning] lambda_l2 is set=8.406074577518491e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.406074577518491e-06
[LightGBM] [Warning] lambda_l1 is set=0.00034706522458153666, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00034706522458153666
[LightGBM] [Warning] bagging_fraction is set=0.8916392534530743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8916392534530743
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.48360217111739395, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.48360217111739395
[LightGBM] [Warning] lambda_l2 is set=2.188074884650128e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.188074884650128e-08
[LightGBM] [Warning] lambda_l1 is set=0.0024106221559327713, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0024106221559327713
[LightGBM] [Warning] bagging_fraction is set=0.8924800340869243, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8924800340869243
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.48360217111739395, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.48360217111739395
[LightGBM] [Warning] lambda_l2 is set=2.188074884650128e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.188074884650128e-08
[LightGBM] [Warning] lambda_l1 is set=0.0024106221559327713, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0024106221559327713
[LightGBM] [Warning] bagging_fraction is set=0.8924800340869243, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8924800340869243
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001021 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.48360217111739395, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.48360217111739395
[LightGBM] [Warning] lambda_l2 is set=2.188074884650128e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.188074884650128e-08
[LightGBM] [Warning] lambda_l1 is set=0.0024106221559327713, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0024106221559327713
[LightGBM] [Warning] bagging_fraction is set=0.8924800340869243, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8924800340869243

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7232284151349021, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7232284151349021
[LightGBM] [Warning] lambda_l2 is set=1.7928655079468506e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7928655079468506e-06
[LightGBM] [Warning] lambda_l1 is set=0.0001613637288046245, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0001613637288046245
[LightGBM] [Warning] bagging_fraction is set=0.6876409579240279, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6876409579240279
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7232284151349021, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7232284151349021
[LightGBM] [Warning] lambda_l2 is set=1.7928655079468506e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7928655079468506e-06
[LightGBM] [Warning] lambda_l1 is set=0.0001613637288046245, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0001613637288046245
[LightGBM] [Warning] bagging_fraction is set=0.6876409579240279, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6876409579240279
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7232284151349021, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7232284151349021
[LightGBM] [Warning] lambda_l2 is set=1.7928655079468506e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7928655079468506e-06
[LightGBM] [Warning] lambda_l1 is set=0.0001613637288046245, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0001613637288046245
[LightGBM] [Warning] bagging_fraction is set=0.6876409579240279, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6876409579240279

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8483956579335864, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8483956579335864
[LightGBM] [Warning] lambda_l2 is set=0.0004911781932753929, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0004911781932753929
[LightGBM] [Warning] lambda_l1 is set=0.002038554233923678, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.002038554233923678
[LightGBM] [Warning] bagging_fraction is set=0.7122126775621803, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7122126775621803
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8483956579335864, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8483956579335864
[LightGBM] [Warning] lambda_l2 is set=0.0004911781932753929, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0004911781932753929
[LightGBM] [Warning] lambda_l1 is set=0.002038554233923678, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.002038554233923678
[LightGBM] [Warning] bagging_fraction is set=0.7122126775621803, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7122126775621803
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8483956579335864, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8483956579335864
[LightGBM] [Warning] lambda_l2 is set=0.0004911781932753929, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0004911781932753929
[LightGBM] [Warning] lambda_l1 is set=0.002038554233923678, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.002038554233923678
[LightGBM] [Warning] bagging_fraction is set=0.7122126775621803, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7122126775621803

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7715647664648435, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7715647664648435
[LightGBM] [Warning] lambda_l2 is set=4.990692499364499e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.990692499364499e-06
[LightGBM] [Warning] lambda_l1 is set=0.05213133078281914, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.05213133078281914
[LightGBM] [Warning] bagging_fraction is set=0.6652569308132821, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6652569308132821
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7715647664648435, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7715647664648435
[LightGBM] [Warning] lambda_l2 is set=4.990692499364499e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.990692499364499e-06
[LightGBM] [Warning] lambda_l1 is set=0.05213133078281914, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.05213133078281914
[LightGBM] [Warning] bagging_fraction is set=0.6652569308132821, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6652569308132821
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7715647664648435, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7715647664648435
[LightGBM] [Warning] lambda_l2 is set=4.990692499364499e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.990692499364499e-06
[LightGBM] [Warning] lambda_l1 is set=0.05213133078281914, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.05213133078281914
[LightGBM] [Warning] bagging_fraction is set=0.6652569308132821, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6652569308132821
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6567956769896348, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6567956769896348
[LightGBM] [Warning] lambda_l2 is set=7.482704750154008e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.482704750154008e-07
[LightGBM] [Warning] lambda_l1 is set=0.00015934532492503943, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00015934532492503943
[LightGBM] [Warning] bagging_fraction is set=0.8455155265578865, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8455155265578865
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6567956769896348, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6567956769896348
[LightGBM] [Warning] lambda_l2 is set=7.482704750154008e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.482704750154008e-07
[LightGBM] [Warning] lambda_l1 is set=0.00015934532492503943, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00015934532492503943
[LightGBM] [Warning] bagging_fraction is set=0.8455155265578865, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8455155265578865
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6567956769896348, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6567956769896348
[LightGBM] [Warning] lambda_l2 is set=7.482704750154008e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.482704750154008e-07
[LightGBM] [Warning] lambda_l1 is set=0.00015934532492503943, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00015934532492503943
[LightGBM] [Warning] bagging_fraction is set=0.8455155265578865, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8455155265578865

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.9137352946935134, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9137352946935134
[LightGBM] [Warning] lambda_l2 is set=1.1887932563069444e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1887932563069444e-05
[LightGBM] [Warning] lambda_l1 is set=0.001130625684818522, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.001130625684818522
[LightGBM] [Warning] bagging_fraction is set=0.922736701256256, subsample=1.0 will be ignored. Current value: bagging_fraction=0.922736701256256
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.9137352946935134, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9137352946935134
[LightGBM] [Warning] lambda_l2 is set=1.1887932563069444e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1887932563069444e-05
[LightGBM] [Warning] lambda_l1 is set=0.001130625684818522, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.001130625684818522
[LightGBM] [Warning] bagging_fraction is set=0.922736701256256, subsample=1.0 will be ignored. Current value: bagging_fraction=0.922736701256256
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.9137352946935134, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9137352946935134
[LightGBM] [Warning] lambda_l2 is set=1.1887932563069444e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1887932563069444e-05
[LightGBM] [Warning] lambda_l1 is set=0.001130625684818522, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.001130625684818522
[LightGBM] [Warning] bagging_fraction is set=0.922736701256256, subsample=1.0 will be ignored. Current value: bagging_fraction=0.922736701256256

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.8301114204833624, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8301114204833624
[LightGBM] [Warning] lambda_l2 is set=1.039607072851844e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.039607072851844e-08
[LightGBM] [Warning] lambda_l1 is set=0.00034250741822427594, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00034250741822427594
[LightGBM] [Warning] bagging_fraction is set=0.8182057876276106, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8182057876276106
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.8301114204833624, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8301114204833624
[LightGBM] [Warning] lambda_l2 is set=1.039607072851844e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.039607072851844e-08
[LightGBM] [Warning] lambda_l1 is set=0.00034250741822427594, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00034250741822427594
[LightGBM] [Warning] bagging_fraction is set=0.8182057876276106, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8182057876276106
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.8301114204833624, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8301114204833624
[LightGBM] [Warning] lambda_l2 is set=1.039607072851844e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.039607072851844e-08
[LightGBM] [Warning] lambda_l1 is set=0.00034250741822427594, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00034250741822427594
[LightGBM] [Warning] bagging_fraction is set=0.8182057876276106, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8182057876276106

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7554874355868193, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7554874355868193
[LightGBM] [Warning] lambda_l2 is set=0.00012832685405216553, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00012832685405216553
[LightGBM] [Warning] lambda_l1 is set=0.08091703111049761, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.08091703111049761
[LightGBM] [Warning] bagging_fraction is set=0.9327857212238579, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9327857212238579
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7554874355868193, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7554874355868193
[LightGBM] [Warning] lambda_l2 is set=0.00012832685405216553, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00012832685405216553
[LightGBM] [Warning] lambda_l1 is set=0.08091703111049761, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.08091703111049761
[LightGBM] [Warning] bagging_fraction is set=0.9327857212238579, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9327857212238579
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7554874355868193, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7554874355868193
[LightGBM] [Warning] lambda_l2 is set=0.00012832685405216553, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00012832685405216553
[LightGBM] [Warning] lambda_l1 is set=0.08091703111049761, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.08091703111049761
[LightGBM] [Warning] bagging_fraction is set=0.9327857212238579, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9327857212238579
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.6457559977159305, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6457559977159305
[LightGBM] [Warning] lambda_l2 is set=0.0014905757016827803, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014905757016827803
[LightGBM] [Warning] lambda_l1 is set=0.324678796578962, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.324678796578962
[LightGBM] [Warning] bagging_fraction is set=0.7196834655402546, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7196834655402546
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.6457559977159305, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6457559977159305
[LightGBM] [Warning] lambda_l2 is set=0.0014905757016827803, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014905757016827803
[LightGBM] [Warning] lambda_l1 is set=0.324678796578962, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.324678796578962
[LightGBM] [Warning] bagging_fraction is set=0.7196834655402546, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7196834655402546
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001032 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.6457559977159305, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6457559977159305
[LightGBM] [Warning] lambda_l2 is set=0.0014905757016827803, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014905757016827803
[LightGBM] [Warning] lambda_l1 is set=0.324678796578962, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.324678796578962
[LightGBM] [Warning] bagging_fraction is set=0.7196834655402546, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7196834655402546
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8330071275224392, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8330071275224392
[LightGBM] [Warning] lambda_l2 is set=1.6681381471166736e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.6681381471166736e-05
[LightGBM] [Warning] lambda_l1 is set=0.0029394656776332317, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0029394656776332317
[LightGBM] [Warning] bagging_fraction is set=0.9929007154387436, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9929007154387436
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8330071275224392, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8330071275224392
[LightGBM] [Warning] lambda_l2 is set=1.6681381471166736e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.6681381471166736e-05
[LightGBM] [Warning] lambda_l1 is set=0.0029394656776332317, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0029394656776332317
[LightGBM] [Warning] bagging_fraction is set=0.9929007154387436, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9929007154387436
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8330071275224392, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8330071275224392
[LightGBM] [Warning] lambda_l2 is set=1.6681381471166736e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.6681381471166736e-05
[LightGBM] [Warning] lambda_l1 is set=0.0029394656776332317, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0029394656776332317
[LightGBM] [Warning] bagging_fraction is set=0.9929007154387436, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9929007154387436

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8232284359507804, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8232284359507804
[LightGBM] [Warning] lambda_l2 is set=1.1450331019254785e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1450331019254785e-06
[LightGBM] [Warning] lambda_l1 is set=0.0009832020390032723, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009832020390032723
[LightGBM] [Warning] bagging_fraction is set=0.859047815767586, subsample=1.0 will be ignored. Current value: bagging_fraction=0.859047815767586
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8232284359507804, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8232284359507804
[LightGBM] [Warning] lambda_l2 is set=1.1450331019254785e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1450331019254785e-06
[LightGBM] [Warning] lambda_l1 is set=0.0009832020390032723, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009832020390032723
[LightGBM] [Warning] bagging_fraction is set=0.859047815767586, subsample=1.0 will be ignored. Current value: bagging_fraction=0.859047815767586
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8232284359507804, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8232284359507804
[LightGBM] [Warning] lambda_l2 is set=1.1450331019254785e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1450331019254785e-06
[LightGBM] [Warning] lambda_l1 is set=0.0009832020390032723, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009832020390032723
[LightGBM] [Warning] bagging_fraction is set=0.859047815767586, subsample=1.0 will be ignored. Current value: bagging_fraction=0.859047815767586
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9272672675286889, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9272672675286889
[LightGBM] [Warning] lambda_l2 is set=9.517138756845152e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.517138756845152e-07
[LightGBM] [Warning] lambda_l1 is set=0.0009354877549545977, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009354877549545977
[LightGBM] [Warning] bagging_fraction is set=0.8619396934642976, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8619396934642976
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9272672675286889, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9272672675286889
[LightGBM] [Warning] lambda_l2 is set=9.517138756845152e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.517138756845152e-07
[LightGBM] [Warning] lambda_l1 is set=0.0009354877549545977, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009354877549545977
[LightGBM] [Warning] bagging_fraction is set=0.8619396934642976, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8619396934642976
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9272672675286889, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9272672675286889
[LightGBM] [Warning] lambda_l2 is set=9.517138756845152e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.517138756845152e-07
[LightGBM] [Warning] lambda_l1 is set=0.0009354877549545977, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009354877549545977
[LightGBM] [Warning] bagging_fraction is set=0.8619396934642976, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8619396934642976
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7528342093239776, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7528342093239776
[LightGBM] [Warning] lambda_l2 is set=3.8833485849829654e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.8833485849829654e-06
[LightGBM] [Warning] lambda_l1 is set=8.428552605179018e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.428552605179018e-05
[LightGBM] [Warning] bagging_fraction is set=0.9282685224908501, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9282685224908501
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7528342093239776, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7528342093239776
[LightGBM] [Warning] lambda_l2 is set=3.8833485849829654e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.8833485849829654e-06
[LightGBM] [Warning] lambda_l1 is set=8.428552605179018e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.428552605179018e-05
[LightGBM] [Warning] bagging_fraction is set=0.9282685224908501, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9282685224908501
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7528342093239776, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7528342093239776
[LightGBM] [Warning] lambda_l2 is set=3.8833485849829654e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.8833485849829654e-06
[LightGBM] [Warning] lambda_l1 is set=8.428552605179018e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.428552605179018e-05
[LightGBM] [Warning] bagging_fraction is set=0.9282685224908501, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9282685224908501
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8659184589407629, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8659184589407629
[LightGBM] [Warning] lambda_l2 is set=2.753256536075659e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.753256536075659e-07
[LightGBM] [Warning] lambda_l1 is set=0.004905389328183663, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.004905389328183663
[LightGBM] [Warning] bagging_fraction is set=0.8254270451187523, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8254270451187523
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8659184589407629, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8659184589407629
[LightGBM] [Warning] lambda_l2 is set=2.753256536075659e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.753256536075659e-07
[LightGBM] [Warning] lambda_l1 is set=0.004905389328183663, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.004905389328183663
[LightGBM] [Warning] bagging_fraction is set=0.8254270451187523, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8254270451187523
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8659184589407629, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8659184589407629
[LightGBM] [Warning] lambda_l2 is set=2.753256536075659e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.753256536075659e-07
[LightGBM] [Warning] lambda_l1 is set=0.004905389328183663, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.004905389328183663
[LightGBM] [Warning] bagging_fraction is set=0.8254270451187523, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8254270451187523
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7956007746048362, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7956007746048362
[LightGBM] [Warning] lambda_l2 is set=6.20760534801716e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.20760534801716e-05
[LightGBM] [Warning] lambda_l1 is set=0.0005531675809982271, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005531675809982271
[LightGBM] [Warning] bagging_fraction is set=0.6475296159976898, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6475296159976898
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7956007746048362, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7956007746048362
[LightGBM] [Warning] lambda_l2 is set=6.20760534801716e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.20760534801716e-05
[LightGBM] [Warning] lambda_l1 is set=0.0005531675809982271, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005531675809982271
[LightGBM] [Warning] bagging_fraction is set=0.6475296159976898, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6475296159976898
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7956007746048362, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7956007746048362
[LightGBM] [Warning] lambda_l2 is set=6.20760534801716e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.20760534801716e-05
[LightGBM] [Warning] lambda_l1 is set=0.0005531675809982271, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005531675809982271
[LightGBM] [Warning] bagging_fraction is set=0.6475296159976898, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6475296159976898

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.6692305566831109, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6692305566831109
[LightGBM] [Warning] lambda_l2 is set=2.2631145524440846e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2631145524440846e-05
[LightGBM] [Warning] lambda_l1 is set=0.007667864547075974, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.007667864547075974
[LightGBM] [Warning] bagging_fraction is set=0.7903230225686916, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7903230225686916
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.6692305566831109, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6692305566831109
[LightGBM] [Warning] lambda_l2 is set=2.2631145524440846e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2631145524440846e-05
[LightGBM] [Warning] lambda_l1 is set=0.007667864547075974, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.007667864547075974
[LightGBM] [Warning] bagging_fraction is set=0.7903230225686916, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7903230225686916
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.6692305566831109, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6692305566831109
[LightGBM] [Warning] lambda_l2 is set=2.2631145524440846e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2631145524440846e-05
[LightGBM] [Warning] lambda_l1 is set=0.007667864547075974, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.007667864547075974
[LightGBM] [Warning] bagging_fraction is set=0.7903230225686916, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7903230225686916

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.7003806891523228, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7003806891523228
[LightGBM] [Warning] lambda_l2 is set=0.00014440574136942422, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00014440574136942422
[LightGBM] [Warning] lambda_l1 is set=5.4578375953872404, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.4578375953872404
[LightGBM] [Warning] bagging_fraction is set=0.7305477953895807, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7305477953895807
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.7003806891523228, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7003806891523228
[LightGBM] [Warning] lambda_l2 is set=0.00014440574136942422, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00014440574136942422
[LightGBM] [Warning] lambda_l1 is set=5.4578375953872404, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.4578375953872404
[LightGBM] [Warning] bagging_fraction is set=0.7305477953895807, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7305477953895807
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.7003806891523228, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7003806891523228
[LightGBM] [Warning] lambda_l2 is set=0.00014440574136942422, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00014440574136942422
[LightGBM] [Warning] lambda_l1 is set=5.4578375953872404, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.4578375953872404
[LightGBM] [Warning] bagging_fraction is set=0.7305477953895807, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7305477953895807

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7638172811156638, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7638172811156638
[LightGBM] [Warning] lambda_l2 is set=4.149090066290888e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.149090066290888e-06
[LightGBM] [Warning] lambda_l1 is set=2.9681146376077342e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.9681146376077342e-05
[LightGBM] [Warning] bagging_fraction is set=0.948515161071952, subsample=1.0 will be ignored. Current value: bagging_fraction=0.948515161071952
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7638172811156638, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7638172811156638
[LightGBM] [Warning] lambda_l2 is set=4.149090066290888e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.149090066290888e-06
[LightGBM] [Warning] lambda_l1 is set=2.9681146376077342e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.9681146376077342e-05
[LightGBM] [Warning] bagging_fraction is set=0.948515161071952, subsample=1.0 will be ignored. Current value: bagging_fraction=0.948515161071952
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7638172811156638, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7638172811156638
[LightGBM] [Warning] lambda_l2 is set=4.149090066290888e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.149090066290888e-06
[LightGBM] [Warning] lambda_l1 is set=2.9681146376077342e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.9681146376077342e-05
[LightGBM] [Warning] bagging_fraction is set=0.948515161071952, subsample=1.0 will be ignored. Current value: bagging_fraction=0.948515161071952

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8881774152885933, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8881774152885933
[LightGBM] [Warning] lambda_l2 is set=4.19958221227145e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.19958221227145e-05
[LightGBM] [Warning] lambda_l1 is set=0.00016002242697791294, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00016002242697791294
[LightGBM] [Warning] bagging_fraction is set=0.9989578912425683, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9989578912425683
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8881774152885933, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8881774152885933
[LightGBM] [Warning] lambda_l2 is set=4.19958221227145e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.19958221227145e-05
[LightGBM] [Warning] lambda_l1 is set=0.00016002242697791294, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00016002242697791294
[LightGBM] [Warning] bagging_fraction is set=0.9989578912425683, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9989578912425683
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8881774152885933, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8881774152885933
[LightGBM] [Warning] lambda_l2 is set=4.19958221227145e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.19958221227145e-05
[LightGBM] [Warning] lambda_l1 is set=0.00016002242697791294, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00016002242697791294
[LightGBM] [Warning] bagging_fraction is set=0.9989578912425683, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9989578912425683
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8091064548275985, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8091064548275985
[LightGBM] [Warning] lambda_l2 is set=1.0103196636464317e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0103196636464317e-07
[LightGBM] [Warning] lambda_l1 is set=0.0017872730232499317, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0017872730232499317
[LightGBM] [Warning] bagging_fraction is set=0.8780057910276269, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8780057910276269
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8091064548275985, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8091064548275985
[LightGBM] [Warning] lambda_l2 is set=1.0103196636464317e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0103196636464317e-07
[LightGBM] [Warning] lambda_l1 is set=0.0017872730232499317, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0017872730232499317
[LightGBM] [Warning] bagging_fraction is set=0.8780057910276269, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8780057910276269
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8091064548275985, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8091064548275985
[LightGBM] [Warning] lambda_l2 is set=1.0103196636464317e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0103196636464317e-07
[LightGBM] [Warning] lambda_l1 is set=0.0017872730232499317, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0017872730232499317
[LightGBM] [Warning] bagging_fraction is set=0.8780057910276269, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8780057910276269

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8247904986066809, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8247904986066809
[LightGBM] [Warning] lambda_l2 is set=1.5778558840843327e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5778558840843327e-06
[LightGBM] [Warning] lambda_l1 is set=0.0005726030059540091, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005726030059540091
[LightGBM] [Warning] bagging_fraction is set=0.8849382618922035, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8849382618922035
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8247904986066809, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8247904986066809
[LightGBM] [Warning] lambda_l2 is set=1.5778558840843327e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5778558840843327e-06
[LightGBM] [Warning] lambda_l1 is set=0.0005726030059540091, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005726030059540091
[LightGBM] [Warning] bagging_fraction is set=0.8849382618922035, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8849382618922035
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8247904986066809, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8247904986066809
[LightGBM] [Warning] lambda_l2 is set=1.5778558840843327e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5778558840843327e-06
[LightGBM] [Warning] lambda_l1 is set=0.0005726030059540091, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005726030059540091
[LightGBM] [Warning] bagging_fraction is set=0.8849382618922035, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8849382618922035
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7166105061764828, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7166105061764828
[LightGBM] [Warning] lambda_l2 is set=3.529474981103005e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.529474981103005e-07
[LightGBM] [Warning] lambda_l1 is set=0.0008341137171164871, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0008341137171164871
[LightGBM] [Warning] bagging_fraction is set=0.8467318926568241, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8467318926568241
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7166105061764828, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7166105061764828
[LightGBM] [Warning] lambda_l2 is set=3.529474981103005e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.529474981103005e-07
[LightGBM] [Warning] lambda_l1 is set=0.0008341137171164871, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0008341137171164871
[LightGBM] [Warning] bagging_fraction is set=0.8467318926568241, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8467318926568241
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7166105061764828, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7166105061764828
[LightGBM] [Warning] lambda_l2 is set=3.529474981103005e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.529474981103005e-07
[LightGBM] [Warning] lambda_l1 is set=0.0008341137171164871, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0008341137171164871
[LightGBM] [Warning] bagging_fraction is set=0.8467318926568241, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8467318926568241
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7215496152082241, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7215496152082241
[LightGBM] [Warning] lambda_l2 is set=5.563870902839119e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.563870902839119e-07
[LightGBM] [Warning] lambda_l1 is set=0.010014525283323576, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.010014525283323576
[LightGBM] [Warning] bagging_fraction is set=0.8185903744108136, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8185903744108136
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7215496152082241, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7215496152082241
[LightGBM] [Warning] lambda_l2 is set=5.563870902839119e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.563870902839119e-07
[LightGBM] [Warning] lambda_l1 is set=0.010014525283323576, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.010014525283323576
[LightGBM] [Warning] bagging_fraction is set=0.8185903744108136, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8185903744108136
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7215496152082241, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7215496152082241
[LightGBM] [Warning] lambda_l2 is set=5.563870902839119e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.563870902839119e-07
[LightGBM] [Warning] lambda_l1 is set=0.010014525283323576, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.010014525283323576
[LightGBM] [Warning] bagging_fraction is set=0.8185903744108136, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8185903744108136
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5882435006475701, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5882435006475701
[LightGBM] [Warning] lambda_l2 is set=4.1087211185543033e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.1087211185543033e-07
[LightGBM] [Warning] lambda_l1 is set=0.003908172080982685, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.003908172080982685
[LightGBM] [Warning] bagging_fraction is set=0.6044578389274314, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6044578389274314
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5882435006475701, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5882435006475701
[LightGBM] [Warning] lambda_l2 is set=4.1087211185543033e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.1087211185543033e-07
[LightGBM] [Warning] lambda_l1 is set=0.003908172080982685, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.003908172080982685
[LightGBM] [Warning] bagging_fraction is set=0.6044578389274314, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6044578389274314
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5882435006475701, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5882435006475701
[LightGBM] [Warning] lambda_l2 is set=4.1087211185543033e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.1087211185543033e-07
[LightGBM] [Warning] lambda_l1 is set=0.003908172080982685, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.003908172080982685
[LightGBM] [Warning] bagging_fraction is set=0.6044578389274314, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6044578389274314

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6977886075295384, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6977886075295384
[LightGBM] [Warning] lambda_l2 is set=6.976051200306815e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.976051200306815e-08
[LightGBM] [Warning] lambda_l1 is set=0.001263167205368438, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.001263167205368438
[LightGBM] [Warning] bagging_fraction is set=0.779546085391128, subsample=1.0 will be ignored. Current value: bagging_fraction=0.779546085391128
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6977886075295384, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6977886075295384
[LightGBM] [Warning] lambda_l2 is set=6.976051200306815e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.976051200306815e-08
[LightGBM] [Warning] lambda_l1 is set=0.001263167205368438, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.001263167205368438
[LightGBM] [Warning] bagging_fraction is set=0.779546085391128, subsample=1.0 will be ignored. Current value: bagging_fraction=0.779546085391128
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6977886075295384, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6977886075295384
[LightGBM] [Warning] lambda_l2 is set=6.976051200306815e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.976051200306815e-08
[LightGBM] [Warning] lambda_l1 is set=0.001263167205368438, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.001263167205368438
[LightGBM] [Warning] bagging_fraction is set=0.779546085391128, subsample=1.0 will be ignored. Current value: bagging_fraction=0.779546085391128

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.9517639299184291, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9517639299184291
[LightGBM] [Warning] lambda_l2 is set=1.782686788572783e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.782686788572783e-07
[LightGBM] [Warning] lambda_l1 is set=0.00032928623798578617, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00032928623798578617
[LightGBM] [Warning] bagging_fraction is set=0.6941801091953778, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6941801091953778
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.9517639299184291, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9517639299184291
[LightGBM] [Warning] lambda_l2 is set=1.782686788572783e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.782686788572783e-07
[LightGBM] [Warning] lambda_l1 is set=0.00032928623798578617, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00032928623798578617
[LightGBM] [Warning] bagging_fraction is set=0.6941801091953778, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6941801091953778
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.9517639299184291, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9517639299184291
[LightGBM] [Warning] lambda_l2 is set=1.782686788572783e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.782686788572783e-07
[LightGBM] [Warning] lambda_l1 is set=0.00032928623798578617, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00032928623798578617
[LightGBM] [Warning] bagging_fraction is set=0.6941801091953778, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6941801091953778
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8786157980490039, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8786157980490039
[LightGBM] [Warning] lambda_l2 is set=3.746943065136392e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.746943065136392e-06
[LightGBM] [Warning] lambda_l1 is set=0.019348683378813316, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.019348683378813316
[LightGBM] [Warning] bagging_fraction is set=0.9524912826574926, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9524912826574926
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8786157980490039, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8786157980490039
[LightGBM] [Warning] lambda_l2 is set=3.746943065136392e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.746943065136392e-06
[LightGBM] [Warning] lambda_l1 is set=0.019348683378813316, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.019348683378813316
[LightGBM] [Warning] bagging_fraction is set=0.9524912826574926, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9524912826574926
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8786157980490039, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8786157980490039
[LightGBM] [Warning] lambda_l2 is set=3.746943065136392e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.746943065136392e-06
[LightGBM] [Warning] lambda_l1 is set=0.019348683378813316, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.019348683378813316
[LightGBM] [Warning] bagging_fraction is set=0.9524912826574926, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9524912826574926
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.780586917567676, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.780586917567676
[LightGBM] [Warning] lambda_l2 is set=6.339313411489575e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.339313411489575e-07
[LightGBM] [Warning] lambda_l1 is set=8.589400433074278e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.589400433074278e-06
[LightGBM] [Warning] bagging_fraction is set=0.8405384976682457, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8405384976682457
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.780586917567676, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.780586917567676
[LightGBM] [Warning] lambda_l2 is set=6.339313411489575e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.339313411489575e-07
[LightGBM] [Warning] lambda_l1 is set=8.589400433074278e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.589400433074278e-06
[LightGBM] [Warning] bagging_fraction is set=0.8405384976682457, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8405384976682457
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.780586917567676, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.780586917567676
[LightGBM] [Warning] lambda_l2 is set=6.339313411489575e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.339313411489575e-07
[LightGBM] [Warning] lambda_l1 is set=8.589400433074278e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.589400433074278e-06
[LightGBM] [Warning] bagging_fraction is set=0.8405384976682457, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8405384976682457

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8924535957950388, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8924535957950388
[LightGBM] [Warning] lambda_l2 is set=1.3974340751571836e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3974340751571836e-05
[LightGBM] [Warning] lambda_l1 is set=5.0039642793255296e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.0039642793255296e-05
[LightGBM] [Warning] bagging_fraction is set=0.7381586081336731, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7381586081336731
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8924535957950388, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8924535957950388
[LightGBM] [Warning] lambda_l2 is set=1.3974340751571836e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3974340751571836e-05
[LightGBM] [Warning] lambda_l1 is set=5.0039642793255296e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.0039642793255296e-05
[LightGBM] [Warning] bagging_fraction is set=0.7381586081336731, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7381586081336731
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8924535957950388, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8924535957950388
[LightGBM] [Warning] lambda_l2 is set=1.3974340751571836e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3974340751571836e-05
[LightGBM] [Warning] lambda_l1 is set=5.0039642793255296e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.0039642793255296e-05
[LightGBM] [Warning] bagging_fraction is set=0.7381586081336731, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7381586081336731

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.852761250298386, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.852761250298386
[LightGBM] [Warning] lambda_l2 is set=1.8163480156664833e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8163480156664833e-06
[LightGBM] [Warning] lambda_l1 is set=0.0009407447098519188, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009407447098519188
[LightGBM] [Warning] bagging_fraction is set=0.9103526955499269, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9103526955499269
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.852761250298386, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.852761250298386
[LightGBM] [Warning] lambda_l2 is set=1.8163480156664833e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8163480156664833e-06
[LightGBM] [Warning] lambda_l1 is set=0.0009407447098519188, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009407447098519188
[LightGBM] [Warning] bagging_fraction is set=0.9103526955499269, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9103526955499269
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.852761250298386, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.852761250298386
[LightGBM] [Warning] lambda_l2 is set=1.8163480156664833e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8163480156664833e-06
[LightGBM] [Warning] lambda_l1 is set=0.0009407447098519188, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0009407447098519188
[LightGBM] [Warning] bagging_fraction is set=0.9103526955499269, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9103526955499269

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8187998071768162, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8187998071768162
[LightGBM] [Warning] lambda_l2 is set=1.7506367583952569e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7506367583952569e-06
[LightGBM] [Warning] lambda_l1 is set=0.0005475793659013395, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005475793659013395
[LightGBM] [Warning] bagging_fraction is set=0.8641812749113131, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8641812749113131
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8187998071768162, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8187998071768162
[LightGBM] [Warning] lambda_l2 is set=1.7506367583952569e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7506367583952569e-06
[LightGBM] [Warning] lambda_l1 is set=0.0005475793659013395, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005475793659013395
[LightGBM] [Warning] bagging_fraction is set=0.8641812749113131, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8641812749113131
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8187998071768162, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8187998071768162
[LightGBM] [Warning] lambda_l2 is set=1.7506367583952569e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7506367583952569e-06
[LightGBM] [Warning] lambda_l1 is set=0.0005475793659013395, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0005475793659013395
[LightGBM] [Warning] bagging_fraction is set=0.8641812749113131, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8641812749113131
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7384456141910831, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7384456141910831
[LightGBM] [Warning] lambda_l2 is set=3.329843256179615e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.329843256179615e-07
[LightGBM] [Warning] lambda_l1 is set=0.0002119829107557275, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002119829107557275
[LightGBM] [Warning] bagging_fraction is set=0.7955356176161901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7955356176161901
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7384456141910831, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7384456141910831
[LightGBM] [Warning] lambda_l2 is set=3.329843256179615e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.329843256179615e-07
[LightGBM] [Warning] lambda_l1 is set=0.0002119829107557275, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002119829107557275
[LightGBM] [Warning] bagging_fraction is set=0.7955356176161901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7955356176161901
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7384456141910831, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7384456141910831
[LightGBM] [Warning] lambda_l2 is set=3.329843256179615e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.329843256179615e-07
[LightGBM] [Warning] lambda_l1 is set=0.0002119829107557275, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002119829107557275
[LightGBM] [Warning] bagging_fraction is set=0.7955356176161901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7955356176161901

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.8022867683133446, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8022867683133446
[LightGBM] [Warning] lambda_l2 is set=9.723725221542589e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.723725221542589e-06
[LightGBM] [Warning] lambda_l1 is set=9.188170904326469e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.188170904326469e-05
[LightGBM] [Warning] bagging_fraction is set=0.8537307529403875, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8537307529403875
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.8022867683133446, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8022867683133446
[LightGBM] [Warning] lambda_l2 is set=9.723725221542589e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.723725221542589e-06
[LightGBM] [Warning] lambda_l1 is set=9.188170904326469e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.188170904326469e-05
[LightGBM] [Warning] bagging_fraction is set=0.8537307529403875, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8537307529403875
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.8022867683133446, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8022867683133446
[LightGBM] [Warning] lambda_l2 is set=9.723725221542589e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.723725221542589e-06
[LightGBM] [Warning] lambda_l1 is set=9.188170904326469e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.188170904326469e-05
[LightGBM] [Warning] bagging_fraction is set=0.8537307529403875, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8537307529403875
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7981537465491082, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7981537465491082
[LightGBM] [Warning] lambda_l2 is set=3.171182529097703e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.171182529097703e-05
[LightGBM] [Warning] lambda_l1 is set=0.00011323101515600265, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00011323101515600265
[LightGBM] [Warning] bagging_fraction is set=0.7652295365367363, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7652295365367363
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7981537465491082, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7981537465491082
[LightGBM] [Warning] lambda_l2 is set=3.171182529097703e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.171182529097703e-05
[LightGBM] [Warning] lambda_l1 is set=0.00011323101515600265, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00011323101515600265
[LightGBM] [Warning] bagging_fraction is set=0.7652295365367363, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7652295365367363
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.7981537465491082, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7981537465491082
[LightGBM] [Warning] lambda_l2 is set=3.171182529097703e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.171182529097703e-05
[LightGBM] [Warning] lambda_l1 is set=0.00011323101515600265, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00011323101515600265
[LightGBM] [Warning] bagging_fraction is set=0.7652295365367363, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7652295365367363
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9443182471737975, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9443182471737975
[LightGBM] [Warning] lambda_l2 is set=1.0523362768815806e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0523362768815806e-06
[LightGBM] [Warning] lambda_l1 is set=2.608195950011019e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.608195950011019e-05
[LightGBM] [Warning] bagging_fraction is set=0.8299398204983547, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8299398204983547
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9443182471737975, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9443182471737975
[LightGBM] [Warning] lambda_l2 is set=1.0523362768815806e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0523362768815806e-06
[LightGBM] [Warning] lambda_l1 is set=2.608195950011019e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.608195950011019e-05
[LightGBM] [Warning] bagging_fraction is set=0.8299398204983547, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8299398204983547
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9443182471737975, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9443182471737975
[LightGBM] [Warning] lambda_l2 is set=1.0523362768815806e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0523362768815806e-06
[LightGBM] [Warning] lambda_l1 is set=2.608195950011019e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.608195950011019e-05
[LightGBM] [Warning] bagging_fraction is set=0.8299398204983547, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8299398204983547
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8494550632116818, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8494550632116818
[LightGBM] [Warning] lambda_l2 is set=2.976619620746018e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.976619620746018e-06
[LightGBM] [Warning] lambda_l1 is set=0.0003799901470122306, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0003799901470122306
[LightGBM] [Warning] bagging_fraction is set=0.9638693527754582, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9638693527754582
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8494550632116818, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8494550632116818
[LightGBM] [Warning] lambda_l2 is set=2.976619620746018e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.976619620746018e-06
[LightGBM] [Warning] lambda_l1 is set=0.0003799901470122306, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0003799901470122306
[LightGBM] [Warning] bagging_fraction is set=0.9638693527754582, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9638693527754582
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8494550632116818, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8494550632116818
[LightGBM] [Warning] lambda_l2 is set=2.976619620746018e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.976619620746018e-06
[LightGBM] [Warning] lambda_l1 is set=0.0003799901470122306, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0003799901470122306
[LightGBM] [Warning] bagging_fraction is set=0.9638693527754582, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9638693527754582
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.7735564276662992, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7735564276662992
[LightGBM] [Warning] lambda_l2 is set=5.752831422586089e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.752831422586089e-06
[LightGBM] [Warning] lambda_l1 is set=0.002389379351689634, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.002389379351689634
[LightGBM] [Warning] bagging_fraction is set=0.8629641570250752, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8629641570250752
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.7735564276662992, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7735564276662992
[LightGBM] [Warning] lambda_l2 is set=5.752831422586089e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.752831422586089e-06
[LightGBM] [Warning] lambda_l1 is set=0.002389379351689634, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.002389379351689634
[LightGBM] [Warning] bagging_fraction is set=0.8629641570250752, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8629641570250752
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.7735564276662992, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7735564276662992
[LightGBM] [Warning] lambda_l2 is set=5.752831422586089e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.752831422586089e-06
[LightGBM] [Warning] lambda_l1 is set=0.002389379351689634, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.002389379351689634
[LightGBM] [Warning] bagging_fraction is set=0.8629641570250752, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8629641570250752

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9110243911527726, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9110243911527726
[LightGBM] [Warning] lambda_l2 is set=1.1186918629902032e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1186918629902032e-05
[LightGBM] [Warning] lambda_l1 is set=7.75116233151575e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.75116233151575e-05
[LightGBM] [Warning] bagging_fraction is set=0.9065529322182494, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9065529322182494
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9110243911527726, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9110243911527726
[LightGBM] [Warning] lambda_l2 is set=1.1186918629902032e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1186918629902032e-05
[LightGBM] [Warning] lambda_l1 is set=7.75116233151575e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.75116233151575e-05
[LightGBM] [Warning] bagging_fraction is set=0.9065529322182494, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9065529322182494
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000470 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9110243911527726, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9110243911527726
[LightGBM] [Warning] lambda_l2 is set=1.1186918629902032e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1186918629902032e-05
[LightGBM] [Warning] lambda_l1 is set=7.75116233151575e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.75116233151575e-05
[LightGBM] [Warning] bagging_fraction is set=0.9065529322182494, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9065529322182494

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),
/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.6959505213757963, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6959505213757963
[LightGBM] [Warning] lambda_l2 is set=1.728932548155876e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.728932548155876e-07
[LightGBM] [Warning] lambda_l1 is set=0.00022864193679993455, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00022864193679993455
[LightGBM] [Warning] bagging_fraction is set=0.7927534516758961, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7927534516758961
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.6959505213757963, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6959505213757963
[LightGBM] [Warning] lambda_l2 is set=1.728932548155876e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.728932548155876e-07
[LightGBM] [Warning] lambda_l1 is set=0.00022864193679993455, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00022864193679993455
[LightGBM] [Warning] bagging_fraction is set=0.7927534516758961, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7927534516758961
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.6959505213757963, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6959505213757963
[LightGBM] [Warning] lambda_l2 is set=1.728932548155876e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.728932548155876e-07
[LightGBM] [Warning] lambda_l1 is set=0.00022864193679993455, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00022864193679993455
[LightGBM] [Warning] bagging_fraction is set=0.7927534516758961, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7927534516758961
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6251960322332902, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6251960322332902
[LightGBM] [Warning] lambda_l2 is set=1.1779862488115862e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1779862488115862e-06
[LightGBM] [Warning] lambda_l1 is set=2.392166740070586e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.392166740070586e-06
[LightGBM] [Warning] bagging_fraction is set=0.7558692125454252, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7558692125454252
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6251960322332902, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6251960322332902
[LightGBM] [Warning] lambda_l2 is set=1.1779862488115862e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1779862488115862e-06
[LightGBM] [Warning] lambda_l1 is set=2.392166740070586e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.392166740070586e-06
[LightGBM] [Warning] bagging_fraction is set=0.7558692125454252, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7558692125454252
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6251960322332902, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6251960322332902
[LightGBM] [Warning] lambda_l2 is set=1.1779862488115862e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1779862488115862e-06
[LightGBM] [Warning] lambda_l1 is set=2.392166740070586e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.392166740070586e-06
[LightGBM] [Warning] bagging_fraction is set=0.7558692125454252, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7558692125454252
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6277969848432461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6277969848432461
[LightGBM] [Warning] lambda_l2 is set=2.121848170331132e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.121848170331132e-06
[LightGBM] [Warning] lambda_l1 is set=2.488319853509851e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.488319853509851e-06
[LightGBM] [Warning] bagging_fraction is set=0.7507729892790026, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7507729892790026
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6277969848432461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6277969848432461
[LightGBM] [Warning] lambda_l2 is set=2.121848170331132e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.121848170331132e-06
[LightGBM] [Warning] lambda_l1 is set=2.488319853509851e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.488319853509851e-06
[LightGBM] [Warning] bagging_fraction is set=0.7507729892790026, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7507729892790026
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001080 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6277969848432461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6277969848432461
[LightGBM] [Warning] lambda_l2 is set=2.121848170331132e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.121848170331132e-06
[LightGBM] [Warning] lambda_l1 is set=2.488319853509851e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.488319853509851e-06
[LightGBM] [Warning] bagging_fraction is set=0.7507729892790026, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7507729892790026
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6041262617155045, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6041262617155045
[LightGBM] [Warning] lambda_l2 is set=2.2611152082367833e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2611152082367833e-06
[LightGBM] [Warning] lambda_l1 is set=5.37978601515276e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.37978601515276e-07
[LightGBM] [Warning] bagging_fraction is set=0.7436883623222351, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7436883623222351
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6041262617155045, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6041262617155045
[LightGBM] [Warning] lambda_l2 is set=2.2611152082367833e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2611152082367833e-06
[LightGBM] [Warning] lambda_l1 is set=5.37978601515276e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.37978601515276e-07
[LightGBM] [Warning] bagging_fraction is set=0.7436883623222351, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7436883623222351
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001025 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6041262617155045, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6041262617155045
[LightGBM] [Warning] lambda_l2 is set=2.2611152082367833e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2611152082367833e-06
[LightGBM] [Warning] lambda_l1 is set=5.37978601515276e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.37978601515276e-07
[LightGBM] [Warning] bagging_fraction is set=0.7436883623222351, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7436883623222351

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6723626477426133, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6723626477426133
[LightGBM] [Warning] lambda_l2 is set=8.021879248669923e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.021879248669923e-06
[LightGBM] [Warning] lambda_l1 is set=2.6936186485441156e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.6936186485441156e-06
[LightGBM] [Warning] bagging_fraction is set=0.6870072345985485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6870072345985485
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6723626477426133, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6723626477426133
[LightGBM] [Warning] lambda_l2 is set=8.021879248669923e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.021879248669923e-06
[LightGBM] [Warning] lambda_l1 is set=2.6936186485441156e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.6936186485441156e-06
[LightGBM] [Warning] bagging_fraction is set=0.6870072345985485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6870072345985485
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6723626477426133, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6723626477426133
[LightGBM] [Warning] lambda_l2 is set=8.021879248669923e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.021879248669923e-06
[LightGBM] [Warning] lambda_l1 is set=2.6936186485441156e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.6936186485441156e-06
[LightGBM] [Warning] bagging_fraction is set=0.6870072345985485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6870072345985485

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6319152319986869, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6319152319986869
[LightGBM] [Warning] lambda_l2 is set=5.099389304839401e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.099389304839401e-07
[LightGBM] [Warning] lambda_l1 is set=2.3216869581714323e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.3216869581714323e-06
[LightGBM] [Warning] bagging_fraction is set=0.7583498104136162, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7583498104136162
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6319152319986869, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6319152319986869
[LightGBM] [Warning] lambda_l2 is set=5.099389304839401e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.099389304839401e-07
[LightGBM] [Warning] lambda_l1 is set=2.3216869581714323e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.3216869581714323e-06
[LightGBM] [Warning] bagging_fraction is set=0.7583498104136162, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7583498104136162
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6319152319986869, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6319152319986869
[LightGBM] [Warning] lambda_l2 is set=5.099389304839401e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.099389304839401e-07
[LightGBM] [Warning] lambda_l1 is set=2.3216869581714323e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.3216869581714323e-06
[LightGBM] [Warning] bagging_fraction is set=0.7583498104136162, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7583498104136162
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5365535918453336, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5365535918453336
[LightGBM] [Warning] lambda_l2 is set=1.0373050023950896e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0373050023950896e-06
[LightGBM] [Warning] lambda_l1 is set=2.7754873190339566e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.7754873190339566e-05
[LightGBM] [Warning] bagging_fraction is set=0.804994872181396, subsample=1.0 will be ignored. Current value: bagging_fraction=0.804994872181396
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5365535918453336, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5365535918453336
[LightGBM] [Warning] lambda_l2 is set=1.0373050023950896e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0373050023950896e-06
[LightGBM] [Warning] lambda_l1 is set=2.7754873190339566e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.7754873190339566e-05
[LightGBM] [Warning] bagging_fraction is set=0.804994872181396, subsample=1.0 will be ignored. Current value: bagging_fraction=0.804994872181396
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5365535918453336, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5365535918453336
[LightGBM] [Warning] lambda_l2 is set=1.0373050023950896e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0373050023950896e-06
[LightGBM] [Warning] lambda_l1 is set=2.7754873190339566e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.7754873190339566e-05
[LightGBM] [Warning] bagging_fraction is set=0.804994872181396, subsample=1.0 will be ignored. Current value: bagging_fraction=0.804994872181396

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5190580672116978, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5190580672116978
[LightGBM] [Warning] lambda_l2 is set=9.287916480089912e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.287916480089912e-07
[LightGBM] [Warning] lambda_l1 is set=1.5820372623623767e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5820372623623767e-05
[LightGBM] [Warning] bagging_fraction is set=0.7971662391703288, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7971662391703288
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5190580672116978, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5190580672116978
[LightGBM] [Warning] lambda_l2 is set=9.287916480089912e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.287916480089912e-07
[LightGBM] [Warning] lambda_l1 is set=1.5820372623623767e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5820372623623767e-05
[LightGBM] [Warning] bagging_fraction is set=0.7971662391703288, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7971662391703288
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5190580672116978, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5190580672116978
[LightGBM] [Warning] lambda_l2 is set=9.287916480089912e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.287916480089912e-07
[LightGBM] [Warning] lambda_l1 is set=1.5820372623623767e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5820372623623767e-05
[LightGBM] [Warning] bagging_fraction is set=0.7971662391703288, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7971662391703288
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5044679597476814, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5044679597476814
[LightGBM] [Warning] lambda_l2 is set=1.0744702651188568e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0744702651188568e-06
[LightGBM] [Warning] lambda_l1 is set=1.682815274243943e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.682815274243943e-05
[LightGBM] [Warning] bagging_fraction is set=0.6621896029584253, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6621896029584253
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5044679597476814, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5044679597476814
[LightGBM] [Warning] lambda_l2 is set=1.0744702651188568e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0744702651188568e-06
[LightGBM] [Warning] lambda_l1 is set=1.682815274243943e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.682815274243943e-05
[LightGBM] [Warning] bagging_fraction is set=0.6621896029584253, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6621896029584253
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001027 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5044679597476814, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5044679597476814
[LightGBM] [Warning] lambda_l2 is set=1.0744702651188568e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0744702651188568e-06
[LightGBM] [Warning] lambda_l1 is set=1.682815274243943e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.682815274243943e-05
[LightGBM] [Warning] bagging_fraction is set=0.6621896029584253, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6621896029584253
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.4342806999944119, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4342806999944119
[LightGBM] [Warning] lambda_l2 is set=2.3864221656061893e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.3864221656061893e-06
[LightGBM] [Warning] lambda_l1 is set=3.787467367555425e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.787467367555425e-06
[LightGBM] [Warning] bagging_fraction is set=0.6175472687775253, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6175472687775253
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.4342806999944119, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4342806999944119
[LightGBM] [Warning] lambda_l2 is set=2.3864221656061893e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.3864221656061893e-06
[LightGBM] [Warning] lambda_l1 is set=3.787467367555425e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.787467367555425e-06
[LightGBM] [Warning] bagging_fraction is set=0.6175472687775253, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6175472687775253
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001004 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.4342806999944119, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4342806999944119
[LightGBM] [Warning] lambda_l2 is set=2.3864221656061893e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.3864221656061893e-06
[LightGBM] [Warning] lambda_l1 is set=3.787467367555425e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.787467367555425e-06
[LightGBM] [Warning] bagging_fraction is set=0.6175472687775253, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6175472687775253

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5186575452592701, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5186575452592701
[LightGBM] [Warning] lambda_l2 is set=1.1731448904969454e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1731448904969454e-06
[LightGBM] [Warning] lambda_l1 is set=1.3222934741722292e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.3222934741722292e-06
[LightGBM] [Warning] bagging_fraction is set=0.659479048501485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.659479048501485
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5186575452592701, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5186575452592701
[LightGBM] [Warning] lambda_l2 is set=1.1731448904969454e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1731448904969454e-06
[LightGBM] [Warning] lambda_l1 is set=1.3222934741722292e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.3222934741722292e-06
[LightGBM] [Warning] bagging_fraction is set=0.659479048501485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.659479048501485
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001045 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5186575452592701, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5186575452592701
[LightGBM] [Warning] lambda_l2 is set=1.1731448904969454e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1731448904969454e-06
[LightGBM] [Warning] lambda_l1 is set=1.3222934741722292e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.3222934741722292e-06
[LightGBM] [Warning] bagging_fraction is set=0.659479048501485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.659479048501485
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.44363939638031114, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.44363939638031114
[LightGBM] [Warning] lambda_l2 is set=5.950839173672261e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.950839173672261e-08
[LightGBM] [Warning] lambda_l1 is set=7.236247756367408e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.236247756367408e-06
[LightGBM] [Warning] bagging_fraction is set=0.7112239931768408, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7112239931768408
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.44363939638031114, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.44363939638031114
[LightGBM] [Warning] lambda_l2 is set=5.950839173672261e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.950839173672261e-08
[LightGBM] [Warning] lambda_l1 is set=7.236247756367408e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.236247756367408e-06
[LightGBM] [Warning] bagging_fraction is set=0.7112239931768408, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7112239931768408
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.44363939638031114, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.44363939638031114
[LightGBM] [Warning] lambda_l2 is set=5.950839173672261e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.950839173672261e-08
[LightGBM] [Warning] lambda_l1 is set=7.236247756367408e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.236247756367408e-06
[LightGBM] [Warning] bagging_fraction is set=0.7112239931768408, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7112239931768408

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5626448798519399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5626448798519399
[LightGBM] [Warning] lambda_l2 is set=7.664391394703926e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.664391394703926e-07
[LightGBM] [Warning] lambda_l1 is set=1.921034466270814e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.921034466270814e-05
[LightGBM] [Warning] bagging_fraction is set=0.798842793576516, subsample=1.0 will be ignored. Current value: bagging_fraction=0.798842793576516
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5626448798519399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5626448798519399
[LightGBM] [Warning] lambda_l2 is set=7.664391394703926e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.664391394703926e-07
[LightGBM] [Warning] lambda_l1 is set=1.921034466270814e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.921034466270814e-05
[LightGBM] [Warning] bagging_fraction is set=0.798842793576516, subsample=1.0 will be ignored. Current value: bagging_fraction=0.798842793576516
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5626448798519399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5626448798519399
[LightGBM] [Warning] lambda_l2 is set=7.664391394703926e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.664391394703926e-07
[LightGBM] [Warning] lambda_l1 is set=1.921034466270814e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.921034466270814e-05
[LightGBM] [Warning] bagging_fraction is set=0.798842793576516, subsample=1.0 will be ignored. Current value: bagging_fraction=0.798842793576516
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5648182129756538, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5648182129756538
[LightGBM] [Warning] lambda_l2 is set=8.008214860513252e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.008214860513252e-07
[LightGBM] [Warning] lambda_l1 is set=1.6222729923274104e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6222729923274104e-05
[LightGBM] [Warning] bagging_fraction is set=0.7621553032458224, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7621553032458224
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5648182129756538, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5648182129756538
[LightGBM] [Warning] lambda_l2 is set=8.008214860513252e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.008214860513252e-07
[LightGBM] [Warning] lambda_l1 is set=1.6222729923274104e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6222729923274104e-05
[LightGBM] [Warning] bagging_fraction is set=0.7621553032458224, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7621553032458224
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5648182129756538, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5648182129756538
[LightGBM] [Warning] lambda_l2 is set=8.008214860513252e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.008214860513252e-07
[LightGBM] [Warning] lambda_l1 is set=1.6222729923274104e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6222729923274104e-05
[LightGBM] [Warning] bagging_fraction is set=0.7621553032458224, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7621553032458224
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5620630297678242, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5620630297678242
[LightGBM] [Warning] lambda_l2 is set=3.163422645578591e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.163422645578591e-06
[LightGBM] [Warning] lambda_l1 is set=1.4250336324398411e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4250336324398411e-05
[LightGBM] [Warning] bagging_fraction is set=0.7219818638137163, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7219818638137163
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5620630297678242, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5620630297678242
[LightGBM] [Warning] lambda_l2 is set=3.163422645578591e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.163422645578591e-06
[LightGBM] [Warning] lambda_l1 is set=1.4250336324398411e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4250336324398411e-05
[LightGBM] [Warning] bagging_fraction is set=0.7219818638137163, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7219818638137163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5620630297678242, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5620630297678242
[LightGBM] [Warning] lambda_l2 is set=3.163422645578591e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.163422645578591e-06
[LightGBM] [Warning] lambda_l1 is set=1.4250336324398411e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4250336324398411e-05
[LightGBM] [Warning] bagging_fraction is set=0.7219818638137163, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7219818638137163
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5645250242586796, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5645250242586796
[LightGBM] [Warning] lambda_l2 is set=5.6467310188143436e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.6467310188143436e-06
[LightGBM] [Warning] lambda_l1 is set=1.5605983614531266e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5605983614531266e-05
[LightGBM] [Warning] bagging_fraction is set=0.7033794844117047, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7033794844117047
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5645250242586796, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5645250242586796
[LightGBM] [Warning] lambda_l2 is set=5.6467310188143436e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.6467310188143436e-06
[LightGBM] [Warning] lambda_l1 is set=1.5605983614531266e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5605983614531266e-05
[LightGBM] [Warning] bagging_fraction is set=0.7033794844117047, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7033794844117047
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001053 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5645250242586796, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5645250242586796
[LightGBM] [Warning] lambda_l2 is set=5.6467310188143436e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.6467310188143436e-06
[LightGBM] [Warning] lambda_l1 is set=1.5605983614531266e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5605983614531266e-05
[LightGBM] [Warning] bagging_fraction is set=0.7033794844117047, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7033794844117047
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5384044895144252, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5384044895144252
[LightGBM] [Warning] lambda_l2 is set=6.55021690682635e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.55021690682635e-07
[LightGBM] [Warning] lambda_l1 is set=1.868137433804251e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.868137433804251e-05
[LightGBM] [Warning] bagging_fraction is set=0.7252359334473693, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7252359334473693
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5384044895144252, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5384044895144252
[LightGBM] [Warning] lambda_l2 is set=6.55021690682635e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.55021690682635e-07
[LightGBM] [Warning] lambda_l1 is set=1.868137433804251e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.868137433804251e-05
[LightGBM] [Warning] bagging_fraction is set=0.7252359334473693, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7252359334473693
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001016 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5384044895144252, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5384044895144252
[LightGBM] [Warning] lambda_l2 is set=6.55021690682635e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.55021690682635e-07
[LightGBM] [Warning] lambda_l1 is set=1.868137433804251e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.868137433804251e-05
[LightGBM] [Warning] bagging_fraction is set=0.7252359334473693, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7252359334473693
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.48980719464111167, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.48980719464111167
[LightGBM] [Warning] lambda_l2 is set=1.8368970388194291e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8368970388194291e-07
[LightGBM] [Warning] lambda_l1 is set=4.519118504828067e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.519118504828067e-05
[LightGBM] [Warning] bagging_fraction is set=0.6800210779739602, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6800210779739602
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.48980719464111167, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.48980719464111167
[LightGBM] [Warning] lambda_l2 is set=1.8368970388194291e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8368970388194291e-07
[LightGBM] [Warning] lambda_l1 is set=4.519118504828067e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.519118504828067e-05
[LightGBM] [Warning] bagging_fraction is set=0.6800210779739602, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6800210779739602
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000998 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.48980719464111167, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.48980719464111167
[LightGBM] [Warning] lambda_l2 is set=1.8368970388194291e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8368970388194291e-07
[LightGBM] [Warning] lambda_l1 is set=4.519118504828067e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.519118504828067e-05
[LightGBM] [Warning] bagging_fraction is set=0.6800210779739602, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6800210779739602
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5754497629951636, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5754497629951636
[LightGBM] [Warning] lambda_l2 is set=3.3558351216542647e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.3558351216542647e-06
[LightGBM] [Warning] lambda_l1 is set=7.126824173646444e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.126824173646444e-06
[LightGBM] [Warning] bagging_fraction is set=0.6409577865103411, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6409577865103411
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5754497629951636, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5754497629951636
[LightGBM] [Warning] lambda_l2 is set=3.3558351216542647e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.3558351216542647e-06
[LightGBM] [Warning] lambda_l1 is set=7.126824173646444e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.126824173646444e-06
[LightGBM] [Warning] bagging_fraction is set=0.6409577865103411, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6409577865103411

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001064 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5754497629951636, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5754497629951636
[LightGBM] [Warning] lambda_l2 is set=3.3558351216542647e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.3558351216542647e-06
[LightGBM] [Warning] lambda_l1 is set=7.126824173646444e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.126824173646444e-06
[LightGBM] [Warning] bagging_fraction is set=0.6409577865103411, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6409577865103411

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5979500673032941, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5979500673032941
[LightGBM] [Warning] lambda_l2 is set=3.570888468297059e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.570888468297059e-07
[LightGBM] [Warning] lambda_l1 is set=1.0673054341456471e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0673054341456471e-05
[LightGBM] [Warning] bagging_fraction is set=0.7409259713770775, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7409259713770775
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5979500673032941, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5979500673032941
[LightGBM] [Warning] lambda_l2 is set=3.570888468297059e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.570888468297059e-07
[LightGBM] [Warning] lambda_l1 is set=1.0673054341456471e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0673054341456471e-05
[LightGBM] [Warning] bagging_fraction is set=0.7409259713770775, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7409259713770775
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5979500673032941, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5979500673032941
[LightGBM] [Warning] lambda_l2 is set=3.570888468297059e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.570888468297059e-07
[LightGBM] [Warning] lambda_l1 is set=1.0673054341456471e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0673054341456471e-05
[LightGBM] [Warning] bagging_fraction is set=0.7409259713770775, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7409259713770775

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5596044394155438, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5596044394155438
[LightGBM] [Warning] lambda_l2 is set=1.9625231165193927e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.9625231165193927e-05
[LightGBM] [Warning] lambda_l1 is set=3.313025297026876e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.313025297026876e-05
[LightGBM] [Warning] bagging_fraction is set=0.664380119603591, subsample=1.0 will be ignored. Current value: bagging_fraction=0.664380119603591
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5596044394155438, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5596044394155438
[LightGBM] [Warning] lambda_l2 is set=1.9625231165193927e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.9625231165193927e-05
[LightGBM] [Warning] lambda_l1 is set=3.313025297026876e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.313025297026876e-05
[LightGBM] [Warning] bagging_fraction is set=0.664380119603591, subsample=1.0 will be ignored. Current value: bagging_fraction=0.664380119603591
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001015 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5596044394155438, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5596044394155438
[LightGBM] [Warning] lambda_l2 is set=1.9625231165193927e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.9625231165193927e-05
[LightGBM] [Warning] lambda_l1 is set=3.313025297026876e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.313025297026876e-05
[LightGBM] [Warning] bagging_fraction is set=0.664380119603591, subsample=1.0 will be ignored. Current value: bagging_fraction=0.664380119603591

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5437079343962157, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5437079343962157
[LightGBM] [Warning] lambda_l2 is set=6.546215921634727e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.546215921634727e-07
[LightGBM] [Warning] lambda_l1 is set=4.9292879761463686e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.9292879761463686e-05
[LightGBM] [Warning] bagging_fraction is set=0.7159069915639732, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7159069915639732
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5437079343962157, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5437079343962157
[LightGBM] [Warning] lambda_l2 is set=6.546215921634727e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.546215921634727e-07
[LightGBM] [Warning] lambda_l1 is set=4.9292879761463686e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.9292879761463686e-05
[LightGBM] [Warning] bagging_fraction is set=0.7159069915639732, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7159069915639732
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5437079343962157, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5437079343962157
[LightGBM] [Warning] lambda_l2 is set=6.546215921634727e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.546215921634727e-07
[LightGBM] [Warning] lambda_l1 is set=4.9292879761463686e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.9292879761463686e-05
[LightGBM] [Warning] bagging_fraction is set=0.7159069915639732, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7159069915639732
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.47291295563838803, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.47291295563838803
[LightGBM] [Warning] lambda_l2 is set=2.0506944567405667e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0506944567405667e-06
[LightGBM] [Warning] lambda_l1 is set=4.68690056707642e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.68690056707642e-06
[LightGBM] [Warning] bagging_fraction is set=0.8178771685551126, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8178771685551126
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.47291295563838803, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.47291295563838803
[LightGBM] [Warning] lambda_l2 is set=2.0506944567405667e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0506944567405667e-06
[LightGBM] [Warning] lambda_l1 is set=4.68690056707642e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.68690056707642e-06
[LightGBM] [Warning] bagging_fraction is set=0.8178771685551126, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8178771685551126
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001001 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.47291295563838803, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.47291295563838803
[LightGBM] [Warning] lambda_l2 is set=2.0506944567405667e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0506944567405667e-06
[LightGBM] [Warning] lambda_l1 is set=4.68690056707642e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.68690056707642e-06
[LightGBM] [Warning] bagging_fraction is set=0.8178771685551126, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8178771685551126
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6103240881734966, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6103240881734966
[LightGBM] [Warning] lambda_l2 is set=6.554977738149925e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.554977738149925e-06
[LightGBM] [Warning] lambda_l1 is set=2.326754388569807e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.326754388569807e-05
[LightGBM] [Warning] bagging_fraction is set=0.7769842203254811, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7769842203254811
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6103240881734966, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6103240881734966
[LightGBM] [Warning] lambda_l2 is set=6.554977738149925e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.554977738149925e-06
[LightGBM] [Warning] lambda_l1 is set=2.326754388569807e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.326754388569807e-05
[LightGBM] [Warning] bagging_fraction is set=0.7769842203254811, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7769842203254811
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6103240881734966, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6103240881734966
[LightGBM] [Warning] lambda_l2 is set=6.554977738149925e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.554977738149925e-06
[LightGBM] [Warning] lambda_l1 is set=2.326754388569807e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.326754388569807e-05
[LightGBM] [Warning] bagging_fraction is set=0.7769842203254811, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7769842203254811
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6544173133915078, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6544173133915078
[LightGBM] [Warning] lambda_l2 is set=1.4960758280578546e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4960758280578546e-06
[LightGBM] [Warning] lambda_l1 is set=1.0546720310547896e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0546720310547896e-05
[LightGBM] [Warning] bagging_fraction is set=0.760427857215426, subsample=1.0 will be ignored. Current value: bagging_fraction=0.760427857215426
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6544173133915078, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6544173133915078
[LightGBM] [Warning] lambda_l2 is set=1.4960758280578546e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4960758280578546e-06
[LightGBM] [Warning] lambda_l1 is set=1.0546720310547896e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0546720310547896e-05
[LightGBM] [Warning] bagging_fraction is set=0.760427857215426, subsample=1.0 will be ignored. Current value: bagging_fraction=0.760427857215426
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001015 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6544173133915078, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6544173133915078
[LightGBM] [Warning] lambda_l2 is set=1.4960758280578546e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4960758280578546e-06
[LightGBM] [Warning] lambda_l1 is set=1.0546720310547896e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0546720310547896e-05
[LightGBM] [Warning] bagging_fraction is set=0.760427857215426, subsample=1.0 will be ignored. Current value: bagging_fraction=0.760427857215426
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.57800821368993, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.57800821368993
[LightGBM] [Warning] lambda_l2 is set=3.021841182094026e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.021841182094026e-06
[LightGBM] [Warning] lambda_l1 is set=5.9447983523655654e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.9447983523655654e-05
[LightGBM] [Warning] bagging_fraction is set=0.8101372587230656, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8101372587230656
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.57800821368993, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.57800821368993
[LightGBM] [Warning] lambda_l2 is set=3.021841182094026e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.021841182094026e-06
[LightGBM] [Warning] lambda_l1 is set=5.9447983523655654e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.9447983523655654e-05
[LightGBM] [Warning] bagging_fraction is set=0.8101372587230656, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8101372587230656
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001059 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.57800821368993, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.57800821368993
[LightGBM] [Warning] lambda_l2 is set=3.021841182094026e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.021841182094026e-06
[LightGBM] [Warning] lambda_l1 is set=5.9447983523655654e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.9447983523655654e-05
[LightGBM] [Warning] bagging_fraction is set=0.8101372587230656, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8101372587230656
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.4219620820138713, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4219620820138713
[LightGBM] [Warning] lambda_l2 is set=6.393417497087965e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.393417497087965e-07
[LightGBM] [Warning] lambda_l1 is set=0.00012253413387378658, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00012253413387378658
[LightGBM] [Warning] bagging_fraction is set=0.7771092655981623, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7771092655981623
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.4219620820138713, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4219620820138713
[LightGBM] [Warning] lambda_l2 is set=6.393417497087965e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.393417497087965e-07
[LightGBM] [Warning] lambda_l1 is set=0.00012253413387378658, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00012253413387378658
[LightGBM] [Warning] bagging_fraction is set=0.7771092655981623, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7771092655981623
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000999 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.4219620820138713, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4219620820138713
[LightGBM] [Warning] lambda_l2 is set=6.393417497087965e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.393417497087965e-07
[LightGBM] [Warning] lambda_l1 is set=0.00012253413387378658, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00012253413387378658
[LightGBM] [Warning] bagging_fraction is set=0.7771092655981623, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7771092655981623
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6164901502463235, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6164901502463235
[LightGBM] [Warning] lambda_l2 is set=2.553095867287692e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.553095867287692e-07
[LightGBM] [Warning] lambda_l1 is set=1.4043933181191054e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4043933181191054e-05
[LightGBM] [Warning] bagging_fraction is set=0.8037139776076905, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8037139776076905
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6164901502463235, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6164901502463235
[LightGBM] [Warning] lambda_l2 is set=2.553095867287692e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.553095867287692e-07
[LightGBM] [Warning] lambda_l1 is set=1.4043933181191054e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4043933181191054e-05
[LightGBM] [Warning] bagging_fraction is set=0.8037139776076905, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8037139776076905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001042 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6164901502463235, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6164901502463235
[LightGBM] [Warning] lambda_l2 is set=2.553095867287692e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.553095867287692e-07
[LightGBM] [Warning] lambda_l1 is set=1.4043933181191054e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4043933181191054e-05
[LightGBM] [Warning] bagging_fraction is set=0.8037139776076905, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8037139776076905

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5899606081872316, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5899606081872316
[LightGBM] [Warning] lambda_l2 is set=1.4035212303057754e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4035212303057754e-06
[LightGBM] [Warning] lambda_l1 is set=3.335406928759597e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.335406928759597e-05
[LightGBM] [Warning] bagging_fraction is set=0.7305631913973482, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7305631913973482
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5899606081872316, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5899606081872316
[LightGBM] [Warning] lambda_l2 is set=1.4035212303057754e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4035212303057754e-06
[LightGBM] [Warning] lambda_l1 is set=3.335406928759597e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.335406928759597e-05
[LightGBM] [Warning] bagging_fraction is set=0.7305631913973482, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7305631913973482
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001033 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5899606081872316, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5899606081872316
[LightGBM] [Warning] lambda_l2 is set=1.4035212303057754e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4035212303057754e-06
[LightGBM] [Warning] lambda_l1 is set=3.335406928759597e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.335406928759597e-05
[LightGBM] [Warning] bagging_fraction is set=0.7305631913973482, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7305631913973482
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5933973274064046, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5933973274064046
[LightGBM] [Warning] lambda_l2 is set=3.996976560193473e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.996976560193473e-06
[LightGBM] [Warning] lambda_l1 is set=4.039567925566461e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.039567925566461e-05
[LightGBM] [Warning] bagging_fraction is set=0.6822630530680377, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6822630530680377
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5933973274064046, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5933973274064046
[LightGBM] [Warning] lambda_l2 is set=3.996976560193473e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.996976560193473e-06
[LightGBM] [Warning] lambda_l1 is set=4.039567925566461e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.039567925566461e-05
[LightGBM] [Warning] bagging_fraction is set=0.6822630530680377, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6822630530680377
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.5933973274064046, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5933973274064046
[LightGBM] [Warning] lambda_l2 is set=3.996976560193473e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.996976560193473e-06
[LightGBM] [Warning] lambda_l1 is set=4.039567925566461e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.039567925566461e-05
[LightGBM] [Warning] bagging_fraction is set=0.6822630530680377, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6822630530680377

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.5426661637597312, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5426661637597312
[LightGBM] [Warning] lambda_l2 is set=1.0728860941441483e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0728860941441483e-07
[LightGBM] [Warning] lambda_l1 is set=2.1693569966609297e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1693569966609297e-05
[LightGBM] [Warning] bagging_fraction is set=0.729918205702446, subsample=1.0 will be ignored. Current value: bagging_fraction=0.729918205702446
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.5426661637597312, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5426661637597312
[LightGBM] [Warning] lambda_l2 is set=1.0728860941441483e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0728860941441483e-07
[LightGBM] [Warning] lambda_l1 is set=2.1693569966609297e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1693569966609297e-05
[LightGBM] [Warning] bagging_fraction is set=0.729918205702446, subsample=1.0 will be ignored. Current value: bagging_fraction=0.729918205702446
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.5426661637597312, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5426661637597312
[LightGBM] [Warning] lambda_l2 is set=1.0728860941441483e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0728860941441483e-07
[LightGBM] [Warning] lambda_l1 is set=2.1693569966609297e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1693569966609297e-05
[LightGBM] [Warning] bagging_fraction is set=0.729918205702446, subsample=1.0 will be ignored. Current value: bagging_fraction=0.729918205702446

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.6387100889623178, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6387100889623178
[LightGBM] [Warning] lambda_l2 is set=8.980124790729938e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.980124790729938e-07
[LightGBM] [Warning] lambda_l1 is set=6.886419474930171e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.886419474930171e-06
[LightGBM] [Warning] bagging_fraction is set=0.6267089185176575, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6267089185176575
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.6387100889623178, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6387100889623178
[LightGBM] [Warning] lambda_l2 is set=8.980124790729938e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.980124790729938e-07
[LightGBM] [Warning] lambda_l1 is set=6.886419474930171e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.886419474930171e-06
[LightGBM] [Warning] bagging_fraction is set=0.6267089185176575, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6267089185176575
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001005 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.6387100889623178, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6387100889623178
[LightGBM] [Warning] lambda_l2 is set=8.980124790729938e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.980124790729938e-07
[LightGBM] [Warning] lambda_l1 is set=6.886419474930171e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.886419474930171e-06
[LightGBM] [Warning] bagging_fraction is set=0.6267089185176575, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6267089185176575

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5665082786453172, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5665082786453172
[LightGBM] [Warning] lambda_l2 is set=1.8255091990359122e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8255091990359122e-06
[LightGBM] [Warning] lambda_l1 is set=7.330303327292208e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.330303327292208e-05
[LightGBM] [Warning] bagging_fraction is set=0.8371057824096031, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8371057824096031
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5665082786453172, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5665082786453172
[LightGBM] [Warning] lambda_l2 is set=1.8255091990359122e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8255091990359122e-06
[LightGBM] [Warning] lambda_l1 is set=7.330303327292208e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.330303327292208e-05
[LightGBM] [Warning] bagging_fraction is set=0.8371057824096031, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8371057824096031
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5665082786453172, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5665082786453172
[LightGBM] [Warning] lambda_l2 is set=1.8255091990359122e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8255091990359122e-06
[LightGBM] [Warning] lambda_l1 is set=7.330303327292208e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.330303327292208e-05
[LightGBM] [Warning] bagging_fraction is set=0.8371057824096031, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8371057824096031

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7439787370570106, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7439787370570106
[LightGBM] [Warning] lambda_l2 is set=1.3446775281225765e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3446775281225765e-06
[LightGBM] [Warning] lambda_l1 is set=3.149681794988501e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.149681794988501e-05
[LightGBM] [Warning] bagging_fraction is set=0.7450218543980592, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7450218543980592
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7439787370570106, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7439787370570106
[LightGBM] [Warning] lambda_l2 is set=1.3446775281225765e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3446775281225765e-06
[LightGBM] [Warning] lambda_l1 is set=3.149681794988501e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.149681794988501e-05
[LightGBM] [Warning] bagging_fraction is set=0.7450218543980592, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7450218543980592
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7439787370570106, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7439787370570106
[LightGBM] [Warning] lambda_l2 is set=1.3446775281225765e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3446775281225765e-06
[LightGBM] [Warning] lambda_l1 is set=3.149681794988501e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.149681794988501e-05
[LightGBM] [Warning] bagging_fraction is set=0.7450218543980592, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7450218543980592
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5861785330825464, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5861785330825464
[LightGBM] [Warning] lambda_l2 is set=4.1597884724312127e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.1597884724312127e-07
[LightGBM] [Warning] lambda_l1 is set=2.833295725355368e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.833295725355368e-05
[LightGBM] [Warning] bagging_fraction is set=0.7026811578254598, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7026811578254598
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5861785330825464, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5861785330825464
[LightGBM] [Warning] lambda_l2 is set=4.1597884724312127e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.1597884724312127e-07
[LightGBM] [Warning] lambda_l1 is set=2.833295725355368e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.833295725355368e-05
[LightGBM] [Warning] bagging_fraction is set=0.7026811578254598, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7026811578254598
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001057 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5861785330825464, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5861785330825464
[LightGBM] [Warning] lambda_l2 is set=4.1597884724312127e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.1597884724312127e-07
[LightGBM] [Warning] lambda_l1 is set=2.833295725355368e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.833295725355368e-05
[LightGBM] [Warning] bagging_fraction is set=0.7026811578254598, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7026811578254598

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7413694768264099, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7413694768264099
[LightGBM] [Warning] lambda_l2 is set=1.041281277238998e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.041281277238998e-06
[LightGBM] [Warning] lambda_l1 is set=1.4765470740864827e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4765470740864827e-05
[LightGBM] [Warning] bagging_fraction is set=0.7394169884054638, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7394169884054638
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7413694768264099, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7413694768264099
[LightGBM] [Warning] lambda_l2 is set=1.041281277238998e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.041281277238998e-06
[LightGBM] [Warning] lambda_l1 is set=1.4765470740864827e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4765470740864827e-05
[LightGBM] [Warning] bagging_fraction is set=0.7394169884054638, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7394169884054638
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7413694768264099, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7413694768264099
[LightGBM] [Warning] lambda_l2 is set=1.041281277238998e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.041281277238998e-06
[LightGBM] [Warning] lambda_l1 is set=1.4765470740864827e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4765470740864827e-05
[LightGBM] [Warning] bagging_fraction is set=0.7394169884054638, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7394169884054638
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5266085155619399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5266085155619399
[LightGBM] [Warning] lambda_l2 is set=8.154025744248935e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.154025744248935e-06
[LightGBM] [Warning] lambda_l1 is set=0.00010882576231670354, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00010882576231670354
[LightGBM] [Warning] bagging_fraction is set=0.7718576575145227, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7718576575145227
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5266085155619399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5266085155619399
[LightGBM] [Warning] lambda_l2 is set=8.154025744248935e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.154025744248935e-06
[LightGBM] [Warning] lambda_l1 is set=0.00010882576231670354, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00010882576231670354
[LightGBM] [Warning] bagging_fraction is set=0.7718576575145227, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7718576575145227
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5266085155619399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5266085155619399
[LightGBM] [Warning] lambda_l2 is set=8.154025744248935e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.154025744248935e-06
[LightGBM] [Warning] lambda_l1 is set=0.00010882576231670354, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00010882576231670354
[LightGBM] [Warning] bagging_fraction is set=0.7718576575145227, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7718576575145227
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5274374818810721, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5274374818810721
[LightGBM] [Warning] lambda_l2 is set=1.280742168138156e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.280742168138156e-05
[LightGBM] [Warning] lambda_l1 is set=0.00021025818005006528, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00021025818005006528
[LightGBM] [Warning] bagging_fraction is set=0.7864763681904742, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7864763681904742
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5274374818810721, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5274374818810721
[LightGBM] [Warning] lambda_l2 is set=1.280742168138156e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.280742168138156e-05
[LightGBM] [Warning] lambda_l1 is set=0.00021025818005006528, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00021025818005006528
[LightGBM] [Warning] bagging_fraction is set=0.7864763681904742, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7864763681904742
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5274374818810721, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5274374818810721
[LightGBM] [Warning] lambda_l2 is set=1.280742168138156e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.280742168138156e-05
[LightGBM] [Warning] lambda_l1 is set=0.00021025818005006528, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00021025818005006528
[LightGBM] [Warning] bagging_fraction is set=0.7864763681904742, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7864763681904742
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.49868682432214934, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49868682432214934
[LightGBM] [Warning] lambda_l2 is set=7.708302325234811e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.708302325234811e-06
[LightGBM] [Warning] lambda_l1 is set=9.86357367270995e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.86357367270995e-05
[LightGBM] [Warning] bagging_fraction is set=0.748648381997516, subsample=1.0 will be ignored. Current value: bagging_fraction=0.748648381997516
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.49868682432214934, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49868682432214934
[LightGBM] [Warning] lambda_l2 is set=7.708302325234811e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.708302325234811e-06
[LightGBM] [Warning] lambda_l1 is set=9.86357367270995e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.86357367270995e-05
[LightGBM] [Warning] bagging_fraction is set=0.748648381997516, subsample=1.0 will be ignored. Current value: bagging_fraction=0.748648381997516
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001065 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.49868682432214934, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49868682432214934
[LightGBM] [Warning] lambda_l2 is set=7.708302325234811e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.708302325234811e-06
[LightGBM] [Warning] lambda_l1 is set=9.86357367270995e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.86357367270995e-05
[LightGBM] [Warning] bagging_fraction is set=0.748648381997516, subsample=1.0 will be ignored. Current value: bagging_fraction=0.748648381997516
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5066927450771034, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5066927450771034
[LightGBM] [Warning] lambda_l2 is set=4.936969477640675e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.936969477640675e-06
[LightGBM] [Warning] lambda_l1 is set=3.941825952414771e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.941825952414771e-05
[LightGBM] [Warning] bagging_fraction is set=0.7699412840850653, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7699412840850653
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5066927450771034, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5066927450771034
[LightGBM] [Warning] lambda_l2 is set=4.936969477640675e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.936969477640675e-06
[LightGBM] [Warning] lambda_l1 is set=3.941825952414771e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.941825952414771e-05
[LightGBM] [Warning] bagging_fraction is set=0.7699412840850653, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7699412840850653
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001001 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.5066927450771034, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5066927450771034
[LightGBM] [Warning] lambda_l2 is set=4.936969477640675e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.936969477640675e-06
[LightGBM] [Warning] lambda_l1 is set=3.941825952414771e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.941825952414771e-05
[LightGBM] [Warning] bagging_fraction is set=0.7699412840850653, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7699412840850653
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.47077897635932775, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.47077897635932775
[LightGBM] [Warning] lambda_l2 is set=5.26701961426273e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.26701961426273e-07
[LightGBM] [Warning] lambda_l1 is set=0.00016175499823917343, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00016175499823917343
[LightGBM] [Warning] bagging_fraction is set=0.8122295801923795, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8122295801923795
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.47077897635932775, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.47077897635932775
[LightGBM] [Warning] lambda_l2 is set=5.26701961426273e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.26701961426273e-07
[LightGBM] [Warning] lambda_l1 is set=0.00016175499823917343, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00016175499823917343
[LightGBM] [Warning] bagging_fraction is set=0.8122295801923795, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8122295801923795
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/2166767348.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/2166767348.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/2166767348.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.47077897635932775, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.47077897635932775
[LightGBM] [Warning] lambda_l2 is set=5.26701961426273e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.26701961426273e-07
[LightGBM] [Warning] lambda_l1 is set=0.00016175499823917343, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00016175499823917343
[LightGBM] [Warning] bagging_fraction is set=0.8122295801923795, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8122295801923795
In [111]:
# Imprimindo os melhores parâmetros segundo o Optuna
best_params = study.best_params
print("Melhores Parâmetros:")
print(best_params)

Melhores Parâmetros:
{'lambda_l1': 0.0010490926070253102, 'lambda_l2': 2.0159438782404665e-06, 'num_leaves': 145, 'feature_fraction': 0.8106667396053426, 'bagging_fraction': 0.6671835190993711, 'bagging_freq': 4, 'min_child_samples': 2, 'learning_rate': 0.17289781682749447, 'n_estimators': 601}
In [112]:
# Criando o modelo final com os melhores parâmetros
final_model = lgb.LGBMRegressor(**best_params)
final_model.fit(treino_base_venda[vars_exp], treino_base_venda[vars_resp])

[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 10081, number of used features: 14
[LightGBM] [Info] Start training from score 611281.460966
Out[112]:
LGBMRegressor(bagging_fraction=0.6671835190993711, bagging_freq=4,
              feature_fraction=0.8106667396053426,
              lambda_l1=0.0010490926070253102, lambda_l2=2.0159438782404665e-06,
              learning_rate=0.17289781682749447, min_child_samples=2,
              n_estimators=601, num_leaves=145)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LGBMRegressor(bagging_fraction=0.6671835190993711, bagging_freq=4,
              feature_fraction=0.8106667396053426,
              lambda_l1=0.0010490926070253102, lambda_l2=2.0159438782404665e-06,
              learning_rate=0.17289781682749447, min_child_samples=2,
              n_estimators=601, num_leaves=145)
In [113]:
#CONJUNTO TREINO

y_pred_final_treino = final_model.predict(treino_base_venda[vars_exp])

rmse_final_treino = np.sqrt(mean_squared_error(treino_base_venda[vars_resp], y_pred_final_treino))

print(f"Treino - RMSE do Modelo Final: {rmse_final_treino}")

#CONJUNTO TESTE

# Previsões com o modelo final
y_pred_final = final_model.predict(teste_base_venda[vars_exp])

# Calculando a métrica RMSE com o modelo final
rmse_final = np.sqrt(mean_squared_error(teste_base_venda[vars_resp], y_pred_final))
print(f"Teste - RMSE do Modelo Final: {rmse_final}")

[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
Treino - RMSE do Modelo Final: 25679.097848528327
[LightGBM] [Warning] feature_fraction is set=0.8106667396053426, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8106667396053426
[LightGBM] [Warning] lambda_l2 is set=2.0159438782404665e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0159438782404665e-06
[LightGBM] [Warning] lambda_l1 is set=0.0010490926070253102, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0010490926070253102
[LightGBM] [Warning] bagging_fraction is set=0.6671835190993711, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6671835190993711
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
Teste - RMSE do Modelo Final: 92161.13635313151
In [114]:
# Verificando a importância relativa de cada variável

importancias_variaveis = final_model.feature_importances_

df_venda_lgbm = pd.DataFrame({'Variável': vars_exp, 'Coeficiente': importancias_variaveis})
df_venda_lgbm.sort_values(by='Coeficiente', ascending=False)
Out[114]:
Variável Coeficiente
1 size 27562
0 condo 20086
3 toilets 5188
2 rooms 4725
6 elevator 4465
5 parking 4398
8 swimming_pool 3808
11 district_zone_Leste 3399
4 suites 2733
12 district_zone_Norte 2599
7 furnished 2533
13 district_zone_Oeste 2282
10 district_zone_Centro 1837
9 new 929

Comentários: as variáveis com mais importância (que mais foram utilizadas para tomar decisão) foram tamanho da propriedade, valor do condomínio e se tem ou não elevador. As variáveis que menos contribuiram foram se a propriedade é nova e se ela é mobiliada

In [115]:
# Gráfico de dispersão

GraficoDispersao(teste_base_venda[vars_resp], y_pred_final, 'Gráfico de Dispersão entre Valores Reais e Previstos', 'Valores Reais', 'Valores Previstos')

Cross - Validation¶

In [116]:
lgbm_reg = lgb.LGBMRegressor()
lgbm_scores = cross_val_score(lgbm_reg, treino_base_venda[vars_exp], treino_base_venda[vars_resp], scoring="neg_mean_squared_error", cv=10)

lgbm_rmse_scores = np.sqrt(-lgbm_scores)

display_scores(lgbm_rmse_scores)

[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 522
[LightGBM] [Info] Number of data points in the train set: 9072, number of used features: 14
[LightGBM] [Info] Start training from score 615851.840278
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 526
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 624626.820236
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 527
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 637120.395900
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 636612.050920
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 528
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 637434.337044
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 525
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 624883.758845
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 526
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 591707.070649
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 548001.118373
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 524
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 598397.696903
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 523
[LightGBM] [Info] Number of data points in the train set: 9073, number of used features: 14
[LightGBM] [Info] Start training from score 598180.024248
Scores: [224325.26975162 268075.30346069 129749.95669025 187425.4985001
 166743.36886728 175212.57308239 300619.20771848 423175.79100121
 268785.85157303 277458.9838591 ]
Mean: 242157.18045041416
Standard deviation: 80579.36459353103

Guardando a versão final do modelo escolhido

In [117]:
import pickle
pickle.dump(final_model, open('modelo_venda.pkl', 'wb'))

🤖 3.6 Modelagem Estatística - LightGBM - Aluguel¶

In [118]:
# Função de otimização do Optuna
def objective(trial):
    param = {
        'objective': 'regression',
        'metric': 'rmse',
        'boosting_type': 'gbdt',
        'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
        'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
        'num_leaves': trial.suggest_int('num_leaves', 2, 256),
        'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
        'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
        'bagging_freq': trial.suggest_int('bagging_freq', 1, 10),
        'min_child_samples': trial.suggest_int('min_child_samples', 1, 100),
        'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),
        'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
        'random_state': 42,
    }

    model = lgb.LGBMRegressor(**param)
    model.fit(treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp])
    y_pred = model.predict(teste_base_aluguel[vars_exp])
    rmse = np.sqrt(mean_squared_error(teste_base_aluguel[vars_resp], y_pred))
    return rmse

# Estudo do Optuna
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=100)

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.718863593130152, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.718863593130152
[LightGBM] [Warning] lambda_l2 is set=9.042268647898448e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.042268647898448e-08
[LightGBM] [Warning] lambda_l1 is set=4.141322859757246e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.141322859757246e-08
[LightGBM] [Warning] bagging_fraction is set=0.7053158899983177, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7053158899983177
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.718863593130152, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.718863593130152
[LightGBM] [Warning] lambda_l2 is set=9.042268647898448e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.042268647898448e-08
[LightGBM] [Warning] lambda_l1 is set=4.141322859757246e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.141322859757246e-08
[LightGBM] [Warning] bagging_fraction is set=0.7053158899983177, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7053158899983177
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.718863593130152, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.718863593130152
[LightGBM] [Warning] lambda_l2 is set=9.042268647898448e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=9.042268647898448e-08
[LightGBM] [Warning] lambda_l1 is set=4.141322859757246e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.141322859757246e-08
[LightGBM] [Warning] bagging_fraction is set=0.7053158899983177, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7053158899983177

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9924063673923139, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9924063673923139
[LightGBM] [Warning] lambda_l2 is set=0.0033923166007361043, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0033923166007361043
[LightGBM] [Warning] lambda_l1 is set=4.517852788752842e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.517852788752842e-05
[LightGBM] [Warning] bagging_fraction is set=0.49028920792143316, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49028920792143316
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9924063673923139, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9924063673923139
[LightGBM] [Warning] lambda_l2 is set=0.0033923166007361043, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0033923166007361043
[LightGBM] [Warning] lambda_l1 is set=4.517852788752842e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.517852788752842e-05
[LightGBM] [Warning] bagging_fraction is set=0.49028920792143316, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49028920792143316
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9924063673923139, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9924063673923139
[LightGBM] [Warning] lambda_l2 is set=0.0033923166007361043, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0033923166007361043
[LightGBM] [Warning] lambda_l1 is set=4.517852788752842e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.517852788752842e-05
[LightGBM] [Warning] bagging_fraction is set=0.49028920792143316, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49028920792143316

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.5011398012945378, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5011398012945378
[LightGBM] [Warning] lambda_l2 is set=2.1777444364441218e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1777444364441218e-07
[LightGBM] [Warning] lambda_l1 is set=6.11602021330386e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.11602021330386e-08
[LightGBM] [Warning] bagging_fraction is set=0.774302868627325, subsample=1.0 will be ignored. Current value: bagging_fraction=0.774302868627325
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.5011398012945378, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5011398012945378
[LightGBM] [Warning] lambda_l2 is set=2.1777444364441218e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1777444364441218e-07
[LightGBM] [Warning] lambda_l1 is set=6.11602021330386e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.11602021330386e-08
[LightGBM] [Warning] bagging_fraction is set=0.774302868627325, subsample=1.0 will be ignored. Current value: bagging_fraction=0.774302868627325
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001123 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.5011398012945378, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5011398012945378
[LightGBM] [Warning] lambda_l2 is set=2.1777444364441218e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1777444364441218e-07
[LightGBM] [Warning] lambda_l1 is set=6.11602021330386e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.11602021330386e-08
[LightGBM] [Warning] bagging_fraction is set=0.774302868627325, subsample=1.0 will be ignored. Current value: bagging_fraction=0.774302868627325

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9621074530302318, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9621074530302318
[LightGBM] [Warning] lambda_l2 is set=0.00011994011878573058, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00011994011878573058
[LightGBM] [Warning] lambda_l1 is set=3.6769559519272685, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.6769559519272685
[LightGBM] [Warning] bagging_fraction is set=0.3861020791236113, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3861020791236113
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9621074530302318, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9621074530302318
[LightGBM] [Warning] lambda_l2 is set=0.00011994011878573058, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00011994011878573058
[LightGBM] [Warning] lambda_l1 is set=3.6769559519272685, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.6769559519272685
[LightGBM] [Warning] bagging_fraction is set=0.3861020791236113, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3861020791236113
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9621074530302318, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9621074530302318
[LightGBM] [Warning] lambda_l2 is set=0.00011994011878573058, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00011994011878573058
[LightGBM] [Warning] lambda_l1 is set=3.6769559519272685, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.6769559519272685
[LightGBM] [Warning] bagging_fraction is set=0.3861020791236113, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3861020791236113

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9421768722315887, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9421768722315887
[LightGBM] [Warning] lambda_l2 is set=0.024913200684102032, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.024913200684102032
[LightGBM] [Warning] lambda_l1 is set=1.1867631419899232e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1867631419899232e-06
[LightGBM] [Warning] bagging_fraction is set=0.5160955571055098, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5160955571055098
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9421768722315887, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9421768722315887
[LightGBM] [Warning] lambda_l2 is set=0.024913200684102032, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.024913200684102032
[LightGBM] [Warning] lambda_l1 is set=1.1867631419899232e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1867631419899232e-06
[LightGBM] [Warning] bagging_fraction is set=0.5160955571055098, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5160955571055098
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9421768722315887, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9421768722315887
[LightGBM] [Warning] lambda_l2 is set=0.024913200684102032, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.024913200684102032
[LightGBM] [Warning] lambda_l1 is set=1.1867631419899232e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1867631419899232e-06
[LightGBM] [Warning] bagging_fraction is set=0.5160955571055098, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5160955571055098

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7132760977264091, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7132760977264091
[LightGBM] [Warning] lambda_l2 is set=8.376327722848934e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.376327722848934e-08
[LightGBM] [Warning] lambda_l1 is set=4.037766625187864e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.037766625187864e-08
[LightGBM] [Warning] bagging_fraction is set=0.3513688740332295, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3513688740332295
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7132760977264091, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7132760977264091
[LightGBM] [Warning] lambda_l2 is set=8.376327722848934e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.376327722848934e-08
[LightGBM] [Warning] lambda_l1 is set=4.037766625187864e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.037766625187864e-08
[LightGBM] [Warning] bagging_fraction is set=0.3513688740332295, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3513688740332295
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7132760977264091, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7132760977264091
[LightGBM] [Warning] lambda_l2 is set=8.376327722848934e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.376327722848934e-08
[LightGBM] [Warning] lambda_l1 is set=4.037766625187864e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.037766625187864e-08
[LightGBM] [Warning] bagging_fraction is set=0.3513688740332295, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3513688740332295

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.31475413841843336, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.31475413841843336
[LightGBM] [Warning] lambda_l2 is set=1.4968108056740723, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4968108056740723
[LightGBM] [Warning] lambda_l1 is set=9.714524293931683e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.714524293931683e-08
[LightGBM] [Warning] bagging_fraction is set=0.14800072745928383, subsample=1.0 will be ignored. Current value: bagging_fraction=0.14800072745928383
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.31475413841843336, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.31475413841843336
[LightGBM] [Warning] lambda_l2 is set=1.4968108056740723, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4968108056740723
[LightGBM] [Warning] lambda_l1 is set=9.714524293931683e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.714524293931683e-08
[LightGBM] [Warning] bagging_fraction is set=0.14800072745928383, subsample=1.0 will be ignored. Current value: bagging_fraction=0.14800072745928383
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.31475413841843336, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.31475413841843336
[LightGBM] [Warning] lambda_l2 is set=1.4968108056740723, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4968108056740723
[LightGBM] [Warning] lambda_l1 is set=9.714524293931683e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.714524293931683e-08
[LightGBM] [Warning] bagging_fraction is set=0.14800072745928383, subsample=1.0 will be ignored. Current value: bagging_fraction=0.14800072745928383
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5237031727204844, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5237031727204844
[LightGBM] [Warning] lambda_l2 is set=0.3526390427175528, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.3526390427175528
[LightGBM] [Warning] lambda_l1 is set=6.192479501467987e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.192479501467987e-07
[LightGBM] [Warning] bagging_fraction is set=0.577531711839123, subsample=1.0 will be ignored. Current value: bagging_fraction=0.577531711839123
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5237031727204844, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5237031727204844
[LightGBM] [Warning] lambda_l2 is set=0.3526390427175528, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.3526390427175528
[LightGBM] [Warning] lambda_l1 is set=6.192479501467987e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.192479501467987e-07
[LightGBM] [Warning] bagging_fraction is set=0.577531711839123, subsample=1.0 will be ignored. Current value: bagging_fraction=0.577531711839123
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000460 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5237031727204844, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5237031727204844
[LightGBM] [Warning] lambda_l2 is set=0.3526390427175528, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.3526390427175528
[LightGBM] [Warning] lambda_l1 is set=6.192479501467987e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.192479501467987e-07
[LightGBM] [Warning] bagging_fraction is set=0.577531711839123, subsample=1.0 will be ignored. Current value: bagging_fraction=0.577531711839123

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.381168831517963, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.381168831517963
[LightGBM] [Warning] lambda_l2 is set=1.156102674102967e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.156102674102967e-06
[LightGBM] [Warning] lambda_l1 is set=5.636762344604907e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.636762344604907e-07
[LightGBM] [Warning] bagging_fraction is set=0.18208764794071988, subsample=1.0 will be ignored. Current value: bagging_fraction=0.18208764794071988
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.381168831517963, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.381168831517963
[LightGBM] [Warning] lambda_l2 is set=1.156102674102967e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.156102674102967e-06
[LightGBM] [Warning] lambda_l1 is set=5.636762344604907e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.636762344604907e-07
[LightGBM] [Warning] bagging_fraction is set=0.18208764794071988, subsample=1.0 will be ignored. Current value: bagging_fraction=0.18208764794071988
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.381168831517963, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.381168831517963
[LightGBM] [Warning] lambda_l2 is set=1.156102674102967e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.156102674102967e-06
[LightGBM] [Warning] lambda_l1 is set=5.636762344604907e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.636762344604907e-07
[LightGBM] [Warning] bagging_fraction is set=0.18208764794071988, subsample=1.0 will be ignored. Current value: bagging_fraction=0.18208764794071988

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.8753351398447304, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8753351398447304
[LightGBM] [Warning] lambda_l2 is set=7.1212195569628375e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.1212195569628375e-06
[LightGBM] [Warning] lambda_l1 is set=0.00015330794172515238, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00015330794172515238
[LightGBM] [Warning] bagging_fraction is set=0.7099918335170392, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7099918335170392
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.8753351398447304, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8753351398447304
[LightGBM] [Warning] lambda_l2 is set=7.1212195569628375e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.1212195569628375e-06
[LightGBM] [Warning] lambda_l1 is set=0.00015330794172515238, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00015330794172515238
[LightGBM] [Warning] bagging_fraction is set=0.7099918335170392, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7099918335170392
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.8753351398447304, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8753351398447304
[LightGBM] [Warning] lambda_l2 is set=7.1212195569628375e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.1212195569628375e-06
[LightGBM] [Warning] lambda_l1 is set=0.00015330794172515238, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00015330794172515238
[LightGBM] [Warning] bagging_fraction is set=0.7099918335170392, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7099918335170392
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.1056255974497185, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.1056255974497185
[LightGBM] [Warning] lambda_l2 is set=2.4393695443722576e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4393695443722576e-05
[LightGBM] [Warning] lambda_l1 is set=0.008740805654729455, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.008740805654729455
[LightGBM] [Warning] bagging_fraction is set=0.9614052610809737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9614052610809737
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.1056255974497185, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.1056255974497185
[LightGBM] [Warning] lambda_l2 is set=2.4393695443722576e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4393695443722576e-05
[LightGBM] [Warning] lambda_l1 is set=0.008740805654729455, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.008740805654729455
[LightGBM] [Warning] bagging_fraction is set=0.9614052610809737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9614052610809737
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),
/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.1056255974497185, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.1056255974497185
[LightGBM] [Warning] lambda_l2 is set=2.4393695443722576e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4393695443722576e-05
[LightGBM] [Warning] lambda_l1 is set=0.008740805654729455, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.008740805654729455
[LightGBM] [Warning] bagging_fraction is set=0.9614052610809737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9614052610809737
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.7465718704719142, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7465718704719142
[LightGBM] [Warning] lambda_l2 is set=1.1222146843083452e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1222146843083452e-08
[LightGBM] [Warning] lambda_l1 is set=7.129615162604401e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.129615162604401e-05
[LightGBM] [Warning] bagging_fraction is set=0.7253506235629485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7253506235629485
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.7465718704719142, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7465718704719142
[LightGBM] [Warning] lambda_l2 is set=1.1222146843083452e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1222146843083452e-08
[LightGBM] [Warning] lambda_l1 is set=7.129615162604401e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.129615162604401e-05
[LightGBM] [Warning] bagging_fraction is set=0.7253506235629485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7253506235629485
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.7465718704719142, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7465718704719142
[LightGBM] [Warning] lambda_l2 is set=1.1222146843083452e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1222146843083452e-08
[LightGBM] [Warning] lambda_l1 is set=7.129615162604401e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.129615162604401e-05
[LightGBM] [Warning] bagging_fraction is set=0.7253506235629485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7253506235629485
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7464694197722904, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7464694197722904
[LightGBM] [Warning] lambda_l2 is set=5.228167022172599e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.228167022172599e-06
[LightGBM] [Warning] lambda_l1 is set=1.0621429340726058e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0621429340726058e-08
[LightGBM] [Warning] bagging_fraction is set=0.7053508721584105, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7053508721584105
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7464694197722904, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7464694197722904
[LightGBM] [Warning] lambda_l2 is set=5.228167022172599e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.228167022172599e-06
[LightGBM] [Warning] lambda_l1 is set=1.0621429340726058e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0621429340726058e-08
[LightGBM] [Warning] bagging_fraction is set=0.7053508721584105, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7053508721584105
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000550 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.7464694197722904, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7464694197722904
[LightGBM] [Warning] lambda_l2 is set=5.228167022172599e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.228167022172599e-06
[LightGBM] [Warning] lambda_l1 is set=1.0621429340726058e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0621429340726058e-08
[LightGBM] [Warning] bagging_fraction is set=0.7053508721584105, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7053508721584105
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8509324849546176, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8509324849546176
[LightGBM] [Warning] lambda_l2 is set=3.91654104973198e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.91654104973198e-06
[LightGBM] [Warning] lambda_l1 is set=0.0012613993249776224, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0012613993249776224
[LightGBM] [Warning] bagging_fraction is set=0.8925478470243389, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8925478470243389
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8509324849546176, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8509324849546176
[LightGBM] [Warning] lambda_l2 is set=3.91654104973198e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.91654104973198e-06
[LightGBM] [Warning] lambda_l1 is set=0.0012613993249776224, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0012613993249776224
[LightGBM] [Warning] bagging_fraction is set=0.8925478470243389, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8925478470243389
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8509324849546176, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8509324849546176
[LightGBM] [Warning] lambda_l2 is set=3.91654104973198e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.91654104973198e-06
[LightGBM] [Warning] lambda_l1 is set=0.0012613993249776224, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0012613993249776224
[LightGBM] [Warning] bagging_fraction is set=0.8925478470243389, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8925478470243389

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.6498764750577866, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6498764750577866
[LightGBM] [Warning] lambda_l2 is set=0.00021217820218622852, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00021217820218622852
[LightGBM] [Warning] lambda_l1 is set=2.410569792801693e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.410569792801693e-06
[LightGBM] [Warning] bagging_fraction is set=0.7874976024380205, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7874976024380205
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.6498764750577866, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6498764750577866
[LightGBM] [Warning] lambda_l2 is set=0.00021217820218622852, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00021217820218622852
[LightGBM] [Warning] lambda_l1 is set=2.410569792801693e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.410569792801693e-06
[LightGBM] [Warning] bagging_fraction is set=0.7874976024380205, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7874976024380205
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.6498764750577866, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6498764750577866
[LightGBM] [Warning] lambda_l2 is set=0.00021217820218622852, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.00021217820218622852
[LightGBM] [Warning] lambda_l1 is set=2.410569792801693e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.410569792801693e-06
[LightGBM] [Warning] bagging_fraction is set=0.7874976024380205, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7874976024380205
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.8534471381510238, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8534471381510238
[LightGBM] [Warning] lambda_l2 is set=5.476422726053979e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.476422726053979e-07
[LightGBM] [Warning] lambda_l1 is set=5.493584487969118e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.493584487969118e-06
[LightGBM] [Warning] bagging_fraction is set=0.6551569328976248, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6551569328976248
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.8534471381510238, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8534471381510238
[LightGBM] [Warning] lambda_l2 is set=5.476422726053979e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.476422726053979e-07
[LightGBM] [Warning] lambda_l1 is set=5.493584487969118e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.493584487969118e-06
[LightGBM] [Warning] bagging_fraction is set=0.6551569328976248, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6551569328976248
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.8534471381510238, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8534471381510238
[LightGBM] [Warning] lambda_l2 is set=5.476422726053979e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.476422726053979e-07
[LightGBM] [Warning] lambda_l1 is set=5.493584487969118e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.493584487969118e-06
[LightGBM] [Warning] bagging_fraction is set=0.6551569328976248, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6551569328976248
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8398325422991667, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8398325422991667
[LightGBM] [Warning] lambda_l2 is set=1.0170853566716527e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0170853566716527e-08
[LightGBM] [Warning] lambda_l1 is set=0.006202998546652147, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.006202998546652147
[LightGBM] [Warning] bagging_fraction is set=0.8728363166888422, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8728363166888422
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8398325422991667, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8398325422991667
[LightGBM] [Warning] lambda_l2 is set=1.0170853566716527e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0170853566716527e-08
[LightGBM] [Warning] lambda_l1 is set=0.006202998546652147, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.006202998546652147
[LightGBM] [Warning] bagging_fraction is set=0.8728363166888422, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8728363166888422
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8398325422991667, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8398325422991667
[LightGBM] [Warning] lambda_l2 is set=1.0170853566716527e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0170853566716527e-08
[LightGBM] [Warning] lambda_l1 is set=0.006202998546652147, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.006202998546652147
[LightGBM] [Warning] bagging_fraction is set=0.8728363166888422, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8728363166888422
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.6416164336448966, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6416164336448966
[LightGBM] [Warning] lambda_l2 is set=1.960143793918503e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.960143793918503e-05
[LightGBM] [Warning] lambda_l1 is set=2.1211045492659984e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1211045492659984e-05
[LightGBM] [Warning] bagging_fraction is set=0.6126201550056108, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6126201550056108
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.6416164336448966, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6416164336448966
[LightGBM] [Warning] lambda_l2 is set=1.960143793918503e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.960143793918503e-05
[LightGBM] [Warning] lambda_l1 is set=2.1211045492659984e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1211045492659984e-05
[LightGBM] [Warning] bagging_fraction is set=0.6126201550056108, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6126201550056108
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.6416164336448966, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6416164336448966
[LightGBM] [Warning] lambda_l2 is set=1.960143793918503e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.960143793918503e-05
[LightGBM] [Warning] lambda_l1 is set=2.1211045492659984e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1211045492659984e-05
[LightGBM] [Warning] bagging_fraction is set=0.6126201550056108, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6126201550056108
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6186814270595732, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6186814270595732
[LightGBM] [Warning] lambda_l2 is set=1.0991363880196457e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0991363880196457e-07
[LightGBM] [Warning] lambda_l1 is set=0.00027497383102272836, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00027497383102272836
[LightGBM] [Warning] bagging_fraction is set=0.993699979868908, subsample=1.0 will be ignored. Current value: bagging_fraction=0.993699979868908
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6186814270595732, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6186814270595732
[LightGBM] [Warning] lambda_l2 is set=1.0991363880196457e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0991363880196457e-07
[LightGBM] [Warning] lambda_l1 is set=0.00027497383102272836, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00027497383102272836
[LightGBM] [Warning] bagging_fraction is set=0.993699979868908, subsample=1.0 will be ignored. Current value: bagging_fraction=0.993699979868908
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.6186814270595732, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6186814270595732
[LightGBM] [Warning] lambda_l2 is set=1.0991363880196457e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0991363880196457e-07
[LightGBM] [Warning] lambda_l1 is set=0.00027497383102272836, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.00027497383102272836
[LightGBM] [Warning] bagging_fraction is set=0.993699979868908, subsample=1.0 will be ignored. Current value: bagging_fraction=0.993699979868908
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.794484772055591, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.794484772055591
[LightGBM] [Warning] lambda_l2 is set=0.0012899611134903459, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0012899611134903459
[LightGBM] [Warning] lambda_l1 is set=6.140755204326034e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.140755204326034e-06
[LightGBM] [Warning] bagging_fraction is set=0.8286189883181685, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8286189883181685
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.794484772055591, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.794484772055591
[LightGBM] [Warning] lambda_l2 is set=0.0012899611134903459, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0012899611134903459
[LightGBM] [Warning] lambda_l1 is set=6.140755204326034e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.140755204326034e-06
[LightGBM] [Warning] bagging_fraction is set=0.8286189883181685, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8286189883181685
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.794484772055591, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.794484772055591
[LightGBM] [Warning] lambda_l2 is set=0.0012899611134903459, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0012899611134903459
[LightGBM] [Warning] lambda_l1 is set=6.140755204326034e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.140755204326034e-06
[LightGBM] [Warning] bagging_fraction is set=0.8286189883181685, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8286189883181685

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9114537751962968, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9114537751962968
[LightGBM] [Warning] lambda_l2 is set=1.2727240569009628e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.2727240569009628e-06
[LightGBM] [Warning] lambda_l1 is set=1.4318019364280668e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4318019364280668e-08
[LightGBM] [Warning] bagging_fraction is set=0.6646541789787282, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6646541789787282
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9114537751962968, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9114537751962968
[LightGBM] [Warning] lambda_l2 is set=1.2727240569009628e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.2727240569009628e-06
[LightGBM] [Warning] lambda_l1 is set=1.4318019364280668e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4318019364280668e-08
[LightGBM] [Warning] bagging_fraction is set=0.6646541789787282, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6646541789787282
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9114537751962968, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9114537751962968
[LightGBM] [Warning] lambda_l2 is set=1.2727240569009628e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.2727240569009628e-06
[LightGBM] [Warning] lambda_l1 is set=1.4318019364280668e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4318019364280668e-08
[LightGBM] [Warning] bagging_fraction is set=0.6646541789787282, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6646541789787282

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.8918233615615623, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8918233615615623
[LightGBM] [Warning] lambda_l2 is set=1.7367570696555267e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7367570696555267e-06
[LightGBM] [Warning] lambda_l1 is set=1.1291285140739909e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1291285140739909e-08
[LightGBM] [Warning] bagging_fraction is set=0.6857456662896452, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6857456662896452
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.8918233615615623, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8918233615615623
[LightGBM] [Warning] lambda_l2 is set=1.7367570696555267e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7367570696555267e-06
[LightGBM] [Warning] lambda_l1 is set=1.1291285140739909e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1291285140739909e-08
[LightGBM] [Warning] bagging_fraction is set=0.6857456662896452, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6857456662896452
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.8918233615615623, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8918233615615623
[LightGBM] [Warning] lambda_l2 is set=1.7367570696555267e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7367570696555267e-06
[LightGBM] [Warning] lambda_l1 is set=1.1291285140739909e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1291285140739909e-08
[LightGBM] [Warning] bagging_fraction is set=0.6857456662896452, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6857456662896452

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9387400627469286, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9387400627469286
[LightGBM] [Warning] lambda_l2 is set=1.064971099966791e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.064971099966791e-06
[LightGBM] [Warning] lambda_l1 is set=1.5127802545683226e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5127802545683226e-08
[LightGBM] [Warning] bagging_fraction is set=0.6402779367886164, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6402779367886164
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9387400627469286, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9387400627469286
[LightGBM] [Warning] lambda_l2 is set=1.064971099966791e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.064971099966791e-06
[LightGBM] [Warning] lambda_l1 is set=1.5127802545683226e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5127802545683226e-08
[LightGBM] [Warning] bagging_fraction is set=0.6402779367886164, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6402779367886164
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9387400627469286, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9387400627469286
[LightGBM] [Warning] lambda_l2 is set=1.064971099966791e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.064971099966791e-06
[LightGBM] [Warning] lambda_l1 is set=1.5127802545683226e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5127802545683226e-08
[LightGBM] [Warning] bagging_fraction is set=0.6402779367886164, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6402779367886164

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.8890792327927981, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8890792327927981
[LightGBM] [Warning] lambda_l2 is set=5.840540476391532e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.840540476391532e-08
[LightGBM] [Warning] lambda_l1 is set=1.6412753212146653e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6412753212146653e-07
[LightGBM] [Warning] bagging_fraction is set=0.7692027139824777, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7692027139824777
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.8890792327927981, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8890792327927981
[LightGBM] [Warning] lambda_l2 is set=5.840540476391532e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.840540476391532e-08
[LightGBM] [Warning] lambda_l1 is set=1.6412753212146653e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6412753212146653e-07
[LightGBM] [Warning] bagging_fraction is set=0.7692027139824777, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7692027139824777
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.8890792327927981, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8890792327927981
[LightGBM] [Warning] lambda_l2 is set=5.840540476391532e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.840540476391532e-08
[LightGBM] [Warning] lambda_l1 is set=1.6412753212146653e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6412753212146653e-07
[LightGBM] [Warning] bagging_fraction is set=0.7692027139824777, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7692027139824777

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.7968284340153902, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7968284340153902
[LightGBM] [Warning] lambda_l2 is set=1.2023078405870912e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.2023078405870912e-06
[LightGBM] [Warning] lambda_l1 is set=1.692415401917386e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.692415401917386e-08
[LightGBM] [Warning] bagging_fraction is set=0.6762037225146125, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6762037225146125
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.7968284340153902, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7968284340153902
[LightGBM] [Warning] lambda_l2 is set=1.2023078405870912e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.2023078405870912e-06
[LightGBM] [Warning] lambda_l1 is set=1.692415401917386e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.692415401917386e-08
[LightGBM] [Warning] bagging_fraction is set=0.6762037225146125, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6762037225146125
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.7968284340153902, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7968284340153902
[LightGBM] [Warning] lambda_l2 is set=1.2023078405870912e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.2023078405870912e-06
[LightGBM] [Warning] lambda_l1 is set=1.692415401917386e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.692415401917386e-08
[LightGBM] [Warning] bagging_fraction is set=0.6762037225146125, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6762037225146125

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9224587745647982, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9224587745647982
[LightGBM] [Warning] lambda_l2 is set=3.158849851109825e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.158849851109825e-05
[LightGBM] [Warning] lambda_l1 is set=1.7647658501955903e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.7647658501955903e-07
[LightGBM] [Warning] bagging_fraction is set=0.5701076903930645, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5701076903930645
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9224587745647982, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9224587745647982
[LightGBM] [Warning] lambda_l2 is set=3.158849851109825e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.158849851109825e-05
[LightGBM] [Warning] lambda_l1 is set=1.7647658501955903e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.7647658501955903e-07
[LightGBM] [Warning] bagging_fraction is set=0.5701076903930645, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5701076903930645
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.9224587745647982, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9224587745647982
[LightGBM] [Warning] lambda_l2 is set=3.158849851109825e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.158849851109825e-05
[LightGBM] [Warning] lambda_l1 is set=1.7647658501955903e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.7647658501955903e-07
[LightGBM] [Warning] bagging_fraction is set=0.5701076903930645, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5701076903930645

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.9940406218886819, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9940406218886819
[LightGBM] [Warning] lambda_l2 is set=5.261043576426806e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.261043576426806e-05
[LightGBM] [Warning] lambda_l1 is set=1.4636205318661402e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4636205318661402e-07
[LightGBM] [Warning] bagging_fraction is set=0.585449623600322, subsample=1.0 will be ignored. Current value: bagging_fraction=0.585449623600322
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.9940406218886819, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9940406218886819
[LightGBM] [Warning] lambda_l2 is set=5.261043576426806e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.261043576426806e-05
[LightGBM] [Warning] lambda_l1 is set=1.4636205318661402e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4636205318661402e-07
[LightGBM] [Warning] bagging_fraction is set=0.585449623600322, subsample=1.0 will be ignored. Current value: bagging_fraction=0.585449623600322
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.9940406218886819, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9940406218886819
[LightGBM] [Warning] lambda_l2 is set=5.261043576426806e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.261043576426806e-05
[LightGBM] [Warning] lambda_l1 is set=1.4636205318661402e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.4636205318661402e-07
[LightGBM] [Warning] bagging_fraction is set=0.585449623600322, subsample=1.0 will be ignored. Current value: bagging_fraction=0.585449623600322
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.9222799505026882, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9222799505026882
[LightGBM] [Warning] lambda_l2 is set=1.3112666838546617e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3112666838546617e-05
[LightGBM] [Warning] lambda_l1 is set=4.3490407882882225e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.3490407882882225e-07
[LightGBM] [Warning] bagging_fraction is set=0.5560810220246257, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5560810220246257
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.9222799505026882, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9222799505026882
[LightGBM] [Warning] lambda_l2 is set=1.3112666838546617e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3112666838546617e-05
[LightGBM] [Warning] lambda_l1 is set=4.3490407882882225e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.3490407882882225e-07
[LightGBM] [Warning] bagging_fraction is set=0.5560810220246257, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5560810220246257
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=9, subsample_freq=0 will be ignored. Current value: bagging_freq=9
[LightGBM] [Warning] feature_fraction is set=0.9222799505026882, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9222799505026882
[LightGBM] [Warning] lambda_l2 is set=1.3112666838546617e-05, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3112666838546617e-05
[LightGBM] [Warning] lambda_l1 is set=4.3490407882882225e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.3490407882882225e-07
[LightGBM] [Warning] bagging_fraction is set=0.5560810220246257, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5560810220246257

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7828341406808953, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7828341406808953
[LightGBM] [Warning] lambda_l2 is set=2.923126850040574e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.923126850040574e-07
[LightGBM] [Warning] lambda_l1 is set=1.2399405701090268e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.2399405701090268e-07
[LightGBM] [Warning] bagging_fraction is set=0.4767138321079254, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4767138321079254
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7828341406808953, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7828341406808953
[LightGBM] [Warning] lambda_l2 is set=2.923126850040574e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.923126850040574e-07
[LightGBM] [Warning] lambda_l1 is set=1.2399405701090268e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.2399405701090268e-07
[LightGBM] [Warning] bagging_fraction is set=0.4767138321079254, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4767138321079254
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10
[LightGBM] [Warning] feature_fraction is set=0.7828341406808953, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7828341406808953
[LightGBM] [Warning] lambda_l2 is set=2.923126850040574e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.923126850040574e-07
[LightGBM] [Warning] lambda_l1 is set=1.2399405701090268e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.2399405701090268e-07
[LightGBM] [Warning] bagging_fraction is set=0.4767138321079254, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4767138321079254

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8025088390405332, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8025088390405332
[LightGBM] [Warning] lambda_l2 is set=2.321933814012948e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.321933814012948e-07
[LightGBM] [Warning] lambda_l1 is set=5.187057124886242e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.187057124886242e-08
[LightGBM] [Warning] bagging_fraction is set=0.4796287475350058, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4796287475350058
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8025088390405332, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8025088390405332
[LightGBM] [Warning] lambda_l2 is set=2.321933814012948e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.321933814012948e-07
[LightGBM] [Warning] lambda_l1 is set=5.187057124886242e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.187057124886242e-08
[LightGBM] [Warning] bagging_fraction is set=0.4796287475350058, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4796287475350058
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8025088390405332, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8025088390405332
[LightGBM] [Warning] lambda_l2 is set=2.321933814012948e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.321933814012948e-07
[LightGBM] [Warning] lambda_l1 is set=5.187057124886242e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.187057124886242e-08
[LightGBM] [Warning] bagging_fraction is set=0.4796287475350058, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4796287475350058

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7037965524555129, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7037965524555129
[LightGBM] [Warning] lambda_l2 is set=2.7078845786379824e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.7078845786379824e-07
[LightGBM] [Warning] lambda_l1 is set=2.5888838563275275e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5888838563275275e-06
[LightGBM] [Warning] bagging_fraction is set=0.4611279401159421, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4611279401159421
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7037965524555129, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7037965524555129
[LightGBM] [Warning] lambda_l2 is set=2.7078845786379824e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.7078845786379824e-07
[LightGBM] [Warning] lambda_l1 is set=2.5888838563275275e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5888838563275275e-06
[LightGBM] [Warning] bagging_fraction is set=0.4611279401159421, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4611279401159421
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7037965524555129, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7037965524555129
[LightGBM] [Warning] lambda_l2 is set=2.7078845786379824e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.7078845786379824e-07
[LightGBM] [Warning] lambda_l1 is set=2.5888838563275275e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.5888838563275275e-06
[LightGBM] [Warning] bagging_fraction is set=0.4611279401159421, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4611279401159421

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6935060639034326, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6935060639034326
[LightGBM] [Warning] lambda_l2 is set=2.8580340595486945e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.8580340595486945e-07
[LightGBM] [Warning] lambda_l1 is set=7.012817200377556e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.012817200377556e-08
[LightGBM] [Warning] bagging_fraction is set=0.4717426251475192, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4717426251475192
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6935060639034326, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6935060639034326
[LightGBM] [Warning] lambda_l2 is set=2.8580340595486945e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.8580340595486945e-07
[LightGBM] [Warning] lambda_l1 is set=7.012817200377556e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.012817200377556e-08
[LightGBM] [Warning] bagging_fraction is set=0.4717426251475192, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4717426251475192
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6935060639034326, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6935060639034326
[LightGBM] [Warning] lambda_l2 is set=2.8580340595486945e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.8580340595486945e-07
[LightGBM] [Warning] lambda_l1 is set=7.012817200377556e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.012817200377556e-08
[LightGBM] [Warning] bagging_fraction is set=0.4717426251475192, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4717426251475192

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6955292343588056, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6955292343588056
[LightGBM] [Warning] lambda_l2 is set=3.716620165704679e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.716620165704679e-08
[LightGBM] [Warning] lambda_l1 is set=5.500199879983613e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.500199879983613e-07
[LightGBM] [Warning] bagging_fraction is set=0.4588392754604246, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4588392754604246
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6955292343588056, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6955292343588056
[LightGBM] [Warning] lambda_l2 is set=3.716620165704679e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.716620165704679e-08
[LightGBM] [Warning] lambda_l1 is set=5.500199879983613e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.500199879983613e-07
[LightGBM] [Warning] bagging_fraction is set=0.4588392754604246, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4588392754604246
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6955292343588056, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6955292343588056
[LightGBM] [Warning] lambda_l2 is set=3.716620165704679e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.716620165704679e-08
[LightGBM] [Warning] lambda_l1 is set=5.500199879983613e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.500199879983613e-07
[LightGBM] [Warning] bagging_fraction is set=0.4588392754604246, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4588392754604246
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6003936486495212, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6003936486495212
[LightGBM] [Warning] lambda_l2 is set=2.705808981964508e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.705808981964508e-07
[LightGBM] [Warning] lambda_l1 is set=5.3205607722224605e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.3205607722224605e-08
[LightGBM] [Warning] bagging_fraction is set=0.45423981793938517, subsample=1.0 will be ignored. Current value: bagging_fraction=0.45423981793938517
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6003936486495212, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6003936486495212
[LightGBM] [Warning] lambda_l2 is set=2.705808981964508e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.705808981964508e-07
[LightGBM] [Warning] lambda_l1 is set=5.3205607722224605e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.3205607722224605e-08
[LightGBM] [Warning] bagging_fraction is set=0.45423981793938517, subsample=1.0 will be ignored. Current value: bagging_fraction=0.45423981793938517
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.6003936486495212, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6003936486495212
[LightGBM] [Warning] lambda_l2 is set=2.705808981964508e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.705808981964508e-07
[LightGBM] [Warning] lambda_l1 is set=5.3205607722224605e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.3205607722224605e-08
[LightGBM] [Warning] bagging_fraction is set=0.45423981793938517, subsample=1.0 will be ignored. Current value: bagging_fraction=0.45423981793938517

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.682062891105037, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.682062891105037
[LightGBM] [Warning] lambda_l2 is set=2.9933816766095855e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.9933816766095855e-07
[LightGBM] [Warning] lambda_l1 is set=2.362840043060502e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.362840043060502e-06
[LightGBM] [Warning] bagging_fraction is set=0.40282057338848704, subsample=1.0 will be ignored. Current value: bagging_fraction=0.40282057338848704
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.682062891105037, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.682062891105037
[LightGBM] [Warning] lambda_l2 is set=2.9933816766095855e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.9933816766095855e-07
[LightGBM] [Warning] lambda_l1 is set=2.362840043060502e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.362840043060502e-06
[LightGBM] [Warning] bagging_fraction is set=0.40282057338848704, subsample=1.0 will be ignored. Current value: bagging_fraction=0.40282057338848704
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.682062891105037, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.682062891105037
[LightGBM] [Warning] lambda_l2 is set=2.9933816766095855e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.9933816766095855e-07
[LightGBM] [Warning] lambda_l1 is set=2.362840043060502e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.362840043060502e-06
[LightGBM] [Warning] bagging_fraction is set=0.40282057338848704, subsample=1.0 will be ignored. Current value: bagging_fraction=0.40282057338848704
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7569407921758564, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7569407921758564
[LightGBM] [Warning] lambda_l2 is set=3.374601184760121e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.374601184760121e-08
[LightGBM] [Warning] lambda_l1 is set=1.6645552282819647e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6645552282819647e-07
[LightGBM] [Warning] bagging_fraction is set=0.5146106551775743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5146106551775743
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7569407921758564, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7569407921758564
[LightGBM] [Warning] lambda_l2 is set=3.374601184760121e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.374601184760121e-08
[LightGBM] [Warning] lambda_l1 is set=1.6645552282819647e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6645552282819647e-07
[LightGBM] [Warning] bagging_fraction is set=0.5146106551775743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5146106551775743
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000501 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7569407921758564, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7569407921758564
[LightGBM] [Warning] lambda_l2 is set=3.374601184760121e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.374601184760121e-08
[LightGBM] [Warning] lambda_l1 is set=1.6645552282819647e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6645552282819647e-07
[LightGBM] [Warning] bagging_fraction is set=0.5146106551775743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5146106551775743

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7336896636980204, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7336896636980204
[LightGBM] [Warning] lambda_l2 is set=3.575117004070634e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.575117004070634e-08
[LightGBM] [Warning] lambda_l1 is set=3.2103794948960616e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.2103794948960616e-07
[LightGBM] [Warning] bagging_fraction is set=0.5203723757969574, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5203723757969574
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7336896636980204, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7336896636980204
[LightGBM] [Warning] lambda_l2 is set=3.575117004070634e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.575117004070634e-08
[LightGBM] [Warning] lambda_l1 is set=3.2103794948960616e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.2103794948960616e-07
[LightGBM] [Warning] bagging_fraction is set=0.5203723757969574, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5203723757969574
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7336896636980204, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7336896636980204
[LightGBM] [Warning] lambda_l2 is set=3.575117004070634e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.575117004070634e-08
[LightGBM] [Warning] lambda_l1 is set=3.2103794948960616e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.2103794948960616e-07
[LightGBM] [Warning] bagging_fraction is set=0.5203723757969574, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5203723757969574

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5706753104481085, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5706753104481085
[LightGBM] [Warning] lambda_l2 is set=1.1220920261045266e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1220920261045266e-07
[LightGBM] [Warning] lambda_l1 is set=5.050293027164474e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.050293027164474e-08
[LightGBM] [Warning] bagging_fraction is set=0.3472055834824024, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3472055834824024
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5706753104481085, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5706753104481085
[LightGBM] [Warning] lambda_l2 is set=1.1220920261045266e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1220920261045266e-07
[LightGBM] [Warning] lambda_l1 is set=5.050293027164474e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.050293027164474e-08
[LightGBM] [Warning] bagging_fraction is set=0.3472055834824024, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3472055834824024
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.5706753104481085, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5706753104481085
[LightGBM] [Warning] lambda_l2 is set=1.1220920261045266e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1220920261045266e-07
[LightGBM] [Warning] lambda_l1 is set=5.050293027164474e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.050293027164474e-08
[LightGBM] [Warning] bagging_fraction is set=0.3472055834824024, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3472055834824024
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6692288123631795, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6692288123631795
[LightGBM] [Warning] lambda_l2 is set=3.4829772184850755e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.4829772184850755e-08
[LightGBM] [Warning] lambda_l1 is set=1.2622887764587227e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.2622887764587227e-07
[LightGBM] [Warning] bagging_fraction is set=0.42477970859631753, subsample=1.0 will be ignored. Current value: bagging_fraction=0.42477970859631753
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6692288123631795, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6692288123631795
[LightGBM] [Warning] lambda_l2 is set=3.4829772184850755e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.4829772184850755e-08
[LightGBM] [Warning] lambda_l1 is set=1.2622887764587227e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.2622887764587227e-07
[LightGBM] [Warning] bagging_fraction is set=0.42477970859631753, subsample=1.0 will be ignored. Current value: bagging_fraction=0.42477970859631753
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.6692288123631795, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6692288123631795
[LightGBM] [Warning] lambda_l2 is set=3.4829772184850755e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.4829772184850755e-08
[LightGBM] [Warning] lambda_l1 is set=1.2622887764587227e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.2622887764587227e-07
[LightGBM] [Warning] bagging_fraction is set=0.42477970859631753, subsample=1.0 will be ignored. Current value: bagging_fraction=0.42477970859631753
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7089080601804988, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7089080601804988
[LightGBM] [Warning] lambda_l2 is set=2.972488168396629e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.972488168396629e-07
[LightGBM] [Warning] lambda_l1 is set=1.6941747508765854e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6941747508765854e-06
[LightGBM] [Warning] bagging_fraction is set=0.5224003885864785, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5224003885864785
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7089080601804988, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7089080601804988
[LightGBM] [Warning] lambda_l2 is set=2.972488168396629e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.972488168396629e-07
[LightGBM] [Warning] lambda_l1 is set=1.6941747508765854e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6941747508765854e-06
[LightGBM] [Warning] bagging_fraction is set=0.5224003885864785, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5224003885864785
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7089080601804988, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7089080601804988
[LightGBM] [Warning] lambda_l2 is set=2.972488168396629e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.972488168396629e-07
[LightGBM] [Warning] lambda_l1 is set=1.6941747508765854e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6941747508765854e-06
[LightGBM] [Warning] bagging_fraction is set=0.5224003885864785, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5224003885864785

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7735483757972518, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7735483757972518
[LightGBM] [Warning] lambda_l2 is set=4.135127757563415e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.135127757563415e-06
[LightGBM] [Warning] lambda_l1 is set=6.726977282371186e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.726977282371186e-07
[LightGBM] [Warning] bagging_fraction is set=0.3599660555861901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3599660555861901
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7735483757972518, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7735483757972518
[LightGBM] [Warning] lambda_l2 is set=4.135127757563415e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.135127757563415e-06
[LightGBM] [Warning] lambda_l1 is set=6.726977282371186e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.726977282371186e-07
[LightGBM] [Warning] bagging_fraction is set=0.3599660555861901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3599660555861901
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7735483757972518, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7735483757972518
[LightGBM] [Warning] lambda_l2 is set=4.135127757563415e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.135127757563415e-06
[LightGBM] [Warning] lambda_l1 is set=6.726977282371186e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.726977282371186e-07
[LightGBM] [Warning] bagging_fraction is set=0.3599660555861901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.3599660555861901

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7099143755809881, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099143755809881
[LightGBM] [Warning] lambda_l2 is set=1.0758557959760066e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0758557959760066e-07
[LightGBM] [Warning] lambda_l1 is set=4.13240289383596e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.13240289383596e-08
[LightGBM] [Warning] bagging_fraction is set=0.49467909849067304, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49467909849067304
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7099143755809881, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099143755809881
[LightGBM] [Warning] lambda_l2 is set=1.0758557959760066e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0758557959760066e-07
[LightGBM] [Warning] lambda_l1 is set=4.13240289383596e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.13240289383596e-08
[LightGBM] [Warning] bagging_fraction is set=0.49467909849067304, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49467909849067304
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000559 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7099143755809881, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7099143755809881
[LightGBM] [Warning] lambda_l2 is set=1.0758557959760066e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0758557959760066e-07
[LightGBM] [Warning] lambda_l1 is set=4.13240289383596e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.13240289383596e-08
[LightGBM] [Warning] bagging_fraction is set=0.49467909849067304, subsample=1.0 will be ignored. Current value: bagging_fraction=0.49467909849067304

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7411800268743298, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7411800268743298
[LightGBM] [Warning] lambda_l2 is set=4.6705028747585106e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.6705028747585106e-07
[LightGBM] [Warning] lambda_l1 is set=2.432147681406608e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.432147681406608e-07
[LightGBM] [Warning] bagging_fraction is set=0.529248611802071, subsample=1.0 will be ignored. Current value: bagging_fraction=0.529248611802071
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7411800268743298, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7411800268743298
[LightGBM] [Warning] lambda_l2 is set=4.6705028747585106e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.6705028747585106e-07
[LightGBM] [Warning] lambda_l1 is set=2.432147681406608e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.432147681406608e-07
[LightGBM] [Warning] bagging_fraction is set=0.529248611802071, subsample=1.0 will be ignored. Current value: bagging_fraction=0.529248611802071
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7411800268743298, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7411800268743298
[LightGBM] [Warning] lambda_l2 is set=4.6705028747585106e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.6705028747585106e-07
[LightGBM] [Warning] lambda_l1 is set=2.432147681406608e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.432147681406608e-07
[LightGBM] [Warning] bagging_fraction is set=0.529248611802071, subsample=1.0 will be ignored. Current value: bagging_fraction=0.529248611802071

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.764403210415809, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.764403210415809
[LightGBM] [Warning] lambda_l2 is set=1.8662013543172288e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8662013543172288e-08
[LightGBM] [Warning] lambda_l1 is set=2.743474452163644e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.743474452163644e-07
[LightGBM] [Warning] bagging_fraction is set=0.5304695789985195, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5304695789985195
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.764403210415809, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.764403210415809
[LightGBM] [Warning] lambda_l2 is set=1.8662013543172288e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8662013543172288e-08
[LightGBM] [Warning] lambda_l1 is set=2.743474452163644e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.743474452163644e-07
[LightGBM] [Warning] bagging_fraction is set=0.5304695789985195, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5304695789985195
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.764403210415809, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.764403210415809
[LightGBM] [Warning] lambda_l2 is set=1.8662013543172288e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8662013543172288e-08
[LightGBM] [Warning] lambda_l1 is set=2.743474452163644e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.743474452163644e-07
[LightGBM] [Warning] bagging_fraction is set=0.5304695789985195, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5304695789985195

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7631049258600825, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7631049258600825
[LightGBM] [Warning] lambda_l2 is set=1.4719388336503623e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4719388336503623e-08
[LightGBM] [Warning] lambda_l1 is set=2.657119265878372e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.657119265878372e-07
[LightGBM] [Warning] bagging_fraction is set=0.5368533721396624, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5368533721396624
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7631049258600825, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7631049258600825
[LightGBM] [Warning] lambda_l2 is set=1.4719388336503623e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4719388336503623e-08
[LightGBM] [Warning] lambda_l1 is set=2.657119265878372e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.657119265878372e-07
[LightGBM] [Warning] bagging_fraction is set=0.5368533721396624, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5368533721396624
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000530 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7631049258600825, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7631049258600825
[LightGBM] [Warning] lambda_l2 is set=1.4719388336503623e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4719388336503623e-08
[LightGBM] [Warning] lambda_l1 is set=2.657119265878372e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.657119265878372e-07
[LightGBM] [Warning] bagging_fraction is set=0.5368533721396624, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5368533721396624

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7633130082732384, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7633130082732384
[LightGBM] [Warning] lambda_l2 is set=1.7055891096597248e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7055891096597248e-08
[LightGBM] [Warning] lambda_l1 is set=2.560701944285394e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.560701944285394e-07
[LightGBM] [Warning] bagging_fraction is set=0.6010713729418888, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6010713729418888
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7633130082732384, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7633130082732384
[LightGBM] [Warning] lambda_l2 is set=1.7055891096597248e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7055891096597248e-08
[LightGBM] [Warning] lambda_l1 is set=2.560701944285394e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.560701944285394e-07
[LightGBM] [Warning] bagging_fraction is set=0.6010713729418888, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6010713729418888
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7633130082732384, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7633130082732384
[LightGBM] [Warning] lambda_l2 is set=1.7055891096597248e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7055891096597248e-08
[LightGBM] [Warning] lambda_l1 is set=2.560701944285394e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.560701944285394e-07
[LightGBM] [Warning] bagging_fraction is set=0.6010713729418888, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6010713729418888

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8191437177800354, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8191437177800354
[LightGBM] [Warning] lambda_l2 is set=2.9918105309863235e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.9918105309863235e-08
[LightGBM] [Warning] lambda_l1 is set=5.534578913720967e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.534578913720967e-07
[LightGBM] [Warning] bagging_fraction is set=0.5405307406721006, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5405307406721006
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8191437177800354, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8191437177800354
[LightGBM] [Warning] lambda_l2 is set=2.9918105309863235e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.9918105309863235e-08
[LightGBM] [Warning] lambda_l1 is set=5.534578913720967e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.534578913720967e-07
[LightGBM] [Warning] bagging_fraction is set=0.5405307406721006, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5405307406721006
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8191437177800354, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8191437177800354
[LightGBM] [Warning] lambda_l2 is set=2.9918105309863235e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.9918105309863235e-08
[LightGBM] [Warning] lambda_l1 is set=5.534578913720967e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.534578913720967e-07
[LightGBM] [Warning] bagging_fraction is set=0.5405307406721006, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5405307406721006

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8046306924049695, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8046306924049695
[LightGBM] [Warning] lambda_l2 is set=6.61043307038109e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.61043307038109e-08
[LightGBM] [Warning] lambda_l1 is set=9.957934197750176e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.957934197750176e-07
[LightGBM] [Warning] bagging_fraction is set=0.5312481312077517, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5312481312077517
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8046306924049695, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8046306924049695
[LightGBM] [Warning] lambda_l2 is set=6.61043307038109e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.61043307038109e-08
[LightGBM] [Warning] lambda_l1 is set=9.957934197750176e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.957934197750176e-07
[LightGBM] [Warning] bagging_fraction is set=0.5312481312077517, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5312481312077517
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8046306924049695, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8046306924049695
[LightGBM] [Warning] lambda_l2 is set=6.61043307038109e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.61043307038109e-08
[LightGBM] [Warning] lambda_l1 is set=9.957934197750176e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.957934197750176e-07
[LightGBM] [Warning] bagging_fraction is set=0.5312481312077517, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5312481312077517

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8302668770735654, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8302668770735654
[LightGBM] [Warning] lambda_l2 is set=2.4753785840435446e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4753785840435446e-08
[LightGBM] [Warning] lambda_l1 is set=1.030961264023512e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.030961264023512e-06
[LightGBM] [Warning] bagging_fraction is set=0.6224088697930641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6224088697930641
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8302668770735654, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8302668770735654
[LightGBM] [Warning] lambda_l2 is set=2.4753785840435446e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4753785840435446e-08
[LightGBM] [Warning] lambda_l1 is set=1.030961264023512e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.030961264023512e-06
[LightGBM] [Warning] bagging_fraction is set=0.6224088697930641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6224088697930641
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8302668770735654, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8302668770735654
[LightGBM] [Warning] lambda_l2 is set=2.4753785840435446e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4753785840435446e-08
[LightGBM] [Warning] lambda_l1 is set=1.030961264023512e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.030961264023512e-06
[LightGBM] [Warning] bagging_fraction is set=0.6224088697930641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6224088697930641

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8536745906458979, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8536745906458979
[LightGBM] [Warning] lambda_l2 is set=1.1148249173158259e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1148249173158259e-08
[LightGBM] [Warning] lambda_l1 is set=1.0837413991143227e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0837413991143227e-07
[LightGBM] [Warning] bagging_fraction is set=0.5585308045722075, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5585308045722075
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8536745906458979, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8536745906458979
[LightGBM] [Warning] lambda_l2 is set=1.1148249173158259e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1148249173158259e-08
[LightGBM] [Warning] lambda_l1 is set=1.0837413991143227e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0837413991143227e-07
[LightGBM] [Warning] bagging_fraction is set=0.5585308045722075, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5585308045722075
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8536745906458979, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8536745906458979
[LightGBM] [Warning] lambda_l2 is set=1.1148249173158259e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.1148249173158259e-08
[LightGBM] [Warning] lambda_l1 is set=1.0837413991143227e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0837413991143227e-07
[LightGBM] [Warning] bagging_fraction is set=0.5585308045722075, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5585308045722075

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7416930199572018, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7416930199572018
[LightGBM] [Warning] lambda_l2 is set=8.580655167041309e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.580655167041309e-08
[LightGBM] [Warning] lambda_l1 is set=2.8067845812522844e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.8067845812522844e-08
[LightGBM] [Warning] bagging_fraction is set=0.6062645730181533, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6062645730181533
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7416930199572018, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7416930199572018
[LightGBM] [Warning] lambda_l2 is set=8.580655167041309e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.580655167041309e-08
[LightGBM] [Warning] lambda_l1 is set=2.8067845812522844e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.8067845812522844e-08
[LightGBM] [Warning] bagging_fraction is set=0.6062645730181533, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6062645730181533
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001136 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7416930199572018, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7416930199572018
[LightGBM] [Warning] lambda_l2 is set=8.580655167041309e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.580655167041309e-08
[LightGBM] [Warning] lambda_l1 is set=2.8067845812522844e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.8067845812522844e-08
[LightGBM] [Warning] bagging_fraction is set=0.6062645730181533, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6062645730181533

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7683096773651403, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7683096773651403
[LightGBM] [Warning] lambda_l2 is set=1.5767972310648424e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5767972310648424e-08
[LightGBM] [Warning] lambda_l1 is set=2.988945354859709e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.988945354859709e-07
[LightGBM] [Warning] bagging_fraction is set=0.5414964393412622, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5414964393412622
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7683096773651403, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7683096773651403
[LightGBM] [Warning] lambda_l2 is set=1.5767972310648424e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5767972310648424e-08
[LightGBM] [Warning] lambda_l1 is set=2.988945354859709e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.988945354859709e-07
[LightGBM] [Warning] bagging_fraction is set=0.5414964393412622, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5414964393412622
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7683096773651403, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7683096773651403
[LightGBM] [Warning] lambda_l2 is set=1.5767972310648424e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5767972310648424e-08
[LightGBM] [Warning] lambda_l1 is set=2.988945354859709e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.988945354859709e-07
[LightGBM] [Warning] bagging_fraction is set=0.5414964393412622, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5414964393412622

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.823107045417696, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.823107045417696
[LightGBM] [Warning] lambda_l2 is set=3.041362643619704e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.041362643619704e-08
[LightGBM] [Warning] lambda_l1 is set=3.5011084775566065e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.5011084775566065e-07
[LightGBM] [Warning] bagging_fraction is set=0.5041711192839392, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5041711192839392
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.823107045417696, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.823107045417696
[LightGBM] [Warning] lambda_l2 is set=3.041362643619704e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.041362643619704e-08
[LightGBM] [Warning] lambda_l1 is set=3.5011084775566065e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.5011084775566065e-07
[LightGBM] [Warning] bagging_fraction is set=0.5041711192839392, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5041711192839392
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000525 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.823107045417696, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.823107045417696
[LightGBM] [Warning] lambda_l2 is set=3.041362643619704e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.041362643619704e-08
[LightGBM] [Warning] lambda_l1 is set=3.5011084775566065e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.5011084775566065e-07
[LightGBM] [Warning] bagging_fraction is set=0.5041711192839392, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5041711192839392

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7743488030998141, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7743488030998141
[LightGBM] [Warning] lambda_l2 is set=1.3769622852096732e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3769622852096732e-08
[LightGBM] [Warning] lambda_l1 is set=7.534806351592245e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.534806351592245e-06
[LightGBM] [Warning] bagging_fraction is set=0.4937642688117967, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4937642688117967
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7743488030998141, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7743488030998141
[LightGBM] [Warning] lambda_l2 is set=1.3769622852096732e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3769622852096732e-08
[LightGBM] [Warning] lambda_l1 is set=7.534806351592245e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.534806351592245e-06
[LightGBM] [Warning] bagging_fraction is set=0.4937642688117967, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4937642688117967
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7743488030998141, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7743488030998141
[LightGBM] [Warning] lambda_l2 is set=1.3769622852096732e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3769622852096732e-08
[LightGBM] [Warning] lambda_l1 is set=7.534806351592245e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.534806351592245e-06
[LightGBM] [Warning] bagging_fraction is set=0.4937642688117967, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4937642688117967

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8762343061075726, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8762343061075726
[LightGBM] [Warning] lambda_l2 is set=6.049525570979006e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.049525570979006e-08
[LightGBM] [Warning] lambda_l1 is set=8.875399364318384e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.875399364318384e-08
[LightGBM] [Warning] bagging_fraction is set=0.42431678974420106, subsample=1.0 will be ignored. Current value: bagging_fraction=0.42431678974420106
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8762343061075726, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8762343061075726
[LightGBM] [Warning] lambda_l2 is set=6.049525570979006e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.049525570979006e-08
[LightGBM] [Warning] lambda_l1 is set=8.875399364318384e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.875399364318384e-08
[LightGBM] [Warning] bagging_fraction is set=0.42431678974420106, subsample=1.0 will be ignored. Current value: bagging_fraction=0.42431678974420106
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8762343061075726, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8762343061075726
[LightGBM] [Warning] lambda_l2 is set=6.049525570979006e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=6.049525570979006e-08
[LightGBM] [Warning] lambda_l1 is set=8.875399364318384e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=8.875399364318384e-08
[LightGBM] [Warning] bagging_fraction is set=0.42431678974420106, subsample=1.0 will be ignored. Current value: bagging_fraction=0.42431678974420106

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7391997176429208, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7391997176429208
[LightGBM] [Warning] lambda_l2 is set=7.390731269582852e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.390731269582852e-07
[LightGBM] [Warning] lambda_l1 is set=2.9259148137245617e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.9259148137245617e-08
[LightGBM] [Warning] bagging_fraction is set=0.5062743570916973, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5062743570916973
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7391997176429208, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7391997176429208
[LightGBM] [Warning] lambda_l2 is set=7.390731269582852e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.390731269582852e-07
[LightGBM] [Warning] lambda_l1 is set=2.9259148137245617e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.9259148137245617e-08
[LightGBM] [Warning] bagging_fraction is set=0.5062743570916973, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5062743570916973
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7391997176429208, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7391997176429208
[LightGBM] [Warning] lambda_l2 is set=7.390731269582852e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.390731269582852e-07
[LightGBM] [Warning] lambda_l1 is set=2.9259148137245617e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.9259148137245617e-08
[LightGBM] [Warning] bagging_fraction is set=0.5062743570916973, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5062743570916973

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8361135623120294, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8361135623120294
[LightGBM] [Warning] lambda_l2 is set=5.862925498210311e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.862925498210311e-07
[LightGBM] [Warning] lambda_l1 is set=2.853293287966362e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.853293287966362e-07
[LightGBM] [Warning] bagging_fraction is set=0.5768188650048144, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5768188650048144
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8361135623120294, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8361135623120294
[LightGBM] [Warning] lambda_l2 is set=5.862925498210311e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.862925498210311e-07
[LightGBM] [Warning] lambda_l1 is set=2.853293287966362e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.853293287966362e-07
[LightGBM] [Warning] bagging_fraction is set=0.5768188650048144, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5768188650048144
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000467 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.8361135623120294, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8361135623120294
[LightGBM] [Warning] lambda_l2 is set=5.862925498210311e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.862925498210311e-07
[LightGBM] [Warning] lambda_l1 is set=2.853293287966362e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.853293287966362e-07
[LightGBM] [Warning] bagging_fraction is set=0.5768188650048144, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5768188650048144

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.638411582647413, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.638411582647413
[LightGBM] [Warning] lambda_l2 is set=1.3647616988984217e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3647616988984217e-07
[LightGBM] [Warning] lambda_l1 is set=1.1925029844650796e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1925029844650796e-06
[LightGBM] [Warning] bagging_fraction is set=0.6213338308219782, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6213338308219782
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.638411582647413, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.638411582647413
[LightGBM] [Warning] lambda_l2 is set=1.3647616988984217e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3647616988984217e-07
[LightGBM] [Warning] lambda_l1 is set=1.1925029844650796e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1925029844650796e-06
[LightGBM] [Warning] bagging_fraction is set=0.6213338308219782, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6213338308219782
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000744 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.638411582647413, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.638411582647413
[LightGBM] [Warning] lambda_l2 is set=1.3647616988984217e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3647616988984217e-07
[LightGBM] [Warning] lambda_l1 is set=1.1925029844650796e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1925029844650796e-06
[LightGBM] [Warning] bagging_fraction is set=0.6213338308219782, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6213338308219782

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9592888773651396, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9592888773651396
[LightGBM] [Warning] lambda_l2 is set=1.0544043727711172e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0544043727711172e-08
[LightGBM] [Warning] lambda_l1 is set=2.2015794274955023e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2015794274955023e-05
[LightGBM] [Warning] bagging_fraction is set=0.5556729390664394, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5556729390664394
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9592888773651396, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9592888773651396
[LightGBM] [Warning] lambda_l2 is set=1.0544043727711172e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0544043727711172e-08
[LightGBM] [Warning] lambda_l1 is set=2.2015794274955023e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2015794274955023e-05
[LightGBM] [Warning] bagging_fraction is set=0.5556729390664394, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5556729390664394
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9592888773651396, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9592888773651396
[LightGBM] [Warning] lambda_l2 is set=1.0544043727711172e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0544043727711172e-08
[LightGBM] [Warning] lambda_l1 is set=2.2015794274955023e-05, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2015794274955023e-05
[LightGBM] [Warning] bagging_fraction is set=0.5556729390664394, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5556729390664394
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.6652715906360899, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6652715906360899
[LightGBM] [Warning] lambda_l2 is set=4.707215290397068e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.707215290397068e-08
[LightGBM] [Warning] lambda_l1 is set=9.484593794808012e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.484593794808012e-08
[LightGBM] [Warning] bagging_fraction is set=0.6455643970699969, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6455643970699969
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.6652715906360899, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6652715906360899
[LightGBM] [Warning] lambda_l2 is set=4.707215290397068e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.707215290397068e-08
[LightGBM] [Warning] lambda_l1 is set=9.484593794808012e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.484593794808012e-08
[LightGBM] [Warning] bagging_fraction is set=0.6455643970699969, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6455643970699969
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.6652715906360899, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6652715906360899
[LightGBM] [Warning] lambda_l2 is set=4.707215290397068e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.707215290397068e-08
[LightGBM] [Warning] lambda_l1 is set=9.484593794808012e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.484593794808012e-08
[LightGBM] [Warning] bagging_fraction is set=0.6455643970699969, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6455643970699969

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8610732457485741, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8610732457485741
[LightGBM] [Warning] lambda_l2 is set=2.103790834505258e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.103790834505258e-06
[LightGBM] [Warning] lambda_l1 is set=2.1931496318592164e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1931496318592164e-07
[LightGBM] [Warning] bagging_fraction is set=0.5860375445116147, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5860375445116147
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8610732457485741, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8610732457485741
[LightGBM] [Warning] lambda_l2 is set=2.103790834505258e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.103790834505258e-06
[LightGBM] [Warning] lambda_l1 is set=2.1931496318592164e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1931496318592164e-07
[LightGBM] [Warning] bagging_fraction is set=0.5860375445116147, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5860375445116147
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8610732457485741, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8610732457485741
[LightGBM] [Warning] lambda_l2 is set=2.103790834505258e-06, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.103790834505258e-06
[LightGBM] [Warning] lambda_l1 is set=2.1931496318592164e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1931496318592164e-07
[LightGBM] [Warning] bagging_fraction is set=0.5860375445116147, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5860375445116147
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8206543387785782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8206543387785782
[LightGBM] [Warning] lambda_l2 is set=2.1344015992914822e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1344015992914822e-08
[LightGBM] [Warning] lambda_l1 is set=6.240782516415947e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.240782516415947e-07
[LightGBM] [Warning] bagging_fraction is set=0.5283371176611629, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5283371176611629
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8206543387785782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8206543387785782
[LightGBM] [Warning] lambda_l2 is set=2.1344015992914822e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1344015992914822e-08
[LightGBM] [Warning] lambda_l1 is set=6.240782516415947e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.240782516415947e-07
[LightGBM] [Warning] bagging_fraction is set=0.5283371176611629, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5283371176611629

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000572 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8206543387785782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8206543387785782
[LightGBM] [Warning] lambda_l2 is set=2.1344015992914822e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.1344015992914822e-08
[LightGBM] [Warning] lambda_l1 is set=6.240782516415947e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.240782516415947e-07
[LightGBM] [Warning] bagging_fraction is set=0.5283371176611629, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5283371176611629

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.792320915232853, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.792320915232853
[LightGBM] [Warning] lambda_l2 is set=2.4976102814911715e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4976102814911715e-08
[LightGBM] [Warning] lambda_l1 is set=4.466165742693873e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.466165742693873e-07
[LightGBM] [Warning] bagging_fraction is set=0.5067607410373485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5067607410373485
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.792320915232853, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.792320915232853
[LightGBM] [Warning] lambda_l2 is set=2.4976102814911715e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4976102814911715e-08
[LightGBM] [Warning] lambda_l1 is set=4.466165742693873e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.466165742693873e-07
[LightGBM] [Warning] bagging_fraction is set=0.5067607410373485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5067607410373485
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.792320915232853, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.792320915232853
[LightGBM] [Warning] lambda_l2 is set=2.4976102814911715e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.4976102814911715e-08
[LightGBM] [Warning] lambda_l1 is set=4.466165742693873e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.466165742693873e-07
[LightGBM] [Warning] bagging_fraction is set=0.5067607410373485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5067607410373485

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7293607773758566, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7293607773758566
[LightGBM] [Warning] lambda_l2 is set=1.3519853802014007e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3519853802014007e-07
[LightGBM] [Warning] lambda_l1 is set=3.5666327494903605e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.5666327494903605e-06
[LightGBM] [Warning] bagging_fraction is set=0.5442881224318484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5442881224318484
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7293607773758566, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7293607773758566
[LightGBM] [Warning] lambda_l2 is set=1.3519853802014007e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3519853802014007e-07
[LightGBM] [Warning] lambda_l1 is set=3.5666327494903605e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.5666327494903605e-06
[LightGBM] [Warning] bagging_fraction is set=0.5442881224318484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5442881224318484
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000494 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7293607773758566, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7293607773758566
[LightGBM] [Warning] lambda_l2 is set=1.3519853802014007e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.3519853802014007e-07
[LightGBM] [Warning] lambda_l1 is set=3.5666327494903605e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.5666327494903605e-06
[LightGBM] [Warning] bagging_fraction is set=0.5442881224318484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5442881224318484

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8222496720465372, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8222496720465372
[LightGBM] [Warning] lambda_l2 is set=5.2658645479464645e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.2658645479464645e-08
[LightGBM] [Warning] lambda_l1 is set=1.057900060933932e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.057900060933932e-06
[LightGBM] [Warning] bagging_fraction is set=0.44365594431913635, subsample=1.0 will be ignored. Current value: bagging_fraction=0.44365594431913635
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8222496720465372, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8222496720465372
[LightGBM] [Warning] lambda_l2 is set=5.2658645479464645e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.2658645479464645e-08
[LightGBM] [Warning] lambda_l1 is set=1.057900060933932e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.057900060933932e-06
[LightGBM] [Warning] bagging_fraction is set=0.44365594431913635, subsample=1.0 will be ignored. Current value: bagging_fraction=0.44365594431913635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000596 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8222496720465372, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8222496720465372
[LightGBM] [Warning] lambda_l2 is set=5.2658645479464645e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.2658645479464645e-08
[LightGBM] [Warning] lambda_l1 is set=1.057900060933932e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.057900060933932e-06
[LightGBM] [Warning] bagging_fraction is set=0.44365594431913635, subsample=1.0 will be ignored. Current value: bagging_fraction=0.44365594431913635

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8984277466339803, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8984277466339803
[LightGBM] [Warning] lambda_l2 is set=2.3232542953981934e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.3232542953981934e-08
[LightGBM] [Warning] lambda_l1 is set=2.247459308518064e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.247459308518064e-08
[LightGBM] [Warning] bagging_fraction is set=0.48421868322207706, subsample=1.0 will be ignored. Current value: bagging_fraction=0.48421868322207706
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8984277466339803, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8984277466339803
[LightGBM] [Warning] lambda_l2 is set=2.3232542953981934e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.3232542953981934e-08
[LightGBM] [Warning] lambda_l1 is set=2.247459308518064e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.247459308518064e-08
[LightGBM] [Warning] bagging_fraction is set=0.48421868322207706, subsample=1.0 will be ignored. Current value: bagging_fraction=0.48421868322207706
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000623 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8984277466339803, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8984277466339803
[LightGBM] [Warning] lambda_l2 is set=2.3232542953981934e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.3232542953981934e-08
[LightGBM] [Warning] lambda_l1 is set=2.247459308518064e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.247459308518064e-08
[LightGBM] [Warning] bagging_fraction is set=0.48421868322207706, subsample=1.0 will be ignored. Current value: bagging_fraction=0.48421868322207706

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7772315408610447, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7772315408610447
[LightGBM] [Warning] lambda_l2 is set=1.0226358056758425e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0226358056758425e-08
[LightGBM] [Warning] lambda_l1 is set=1.6071744467157324e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6071744467157324e-07
[LightGBM] [Warning] bagging_fraction is set=0.5830904696843932, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5830904696843932
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7772315408610447, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7772315408610447
[LightGBM] [Warning] lambda_l2 is set=1.0226358056758425e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0226358056758425e-08
[LightGBM] [Warning] lambda_l1 is set=1.6071744467157324e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6071744467157324e-07
[LightGBM] [Warning] bagging_fraction is set=0.5830904696843932, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5830904696843932
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000591 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7772315408610447, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7772315408610447
[LightGBM] [Warning] lambda_l2 is set=1.0226358056758425e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0226358056758425e-08
[LightGBM] [Warning] lambda_l1 is set=1.6071744467157324e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6071744467157324e-07
[LightGBM] [Warning] bagging_fraction is set=0.5830904696843932, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5830904696843932

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8128029103741291, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8128029103741291
[LightGBM] [Warning] lambda_l2 is set=1.5362599015639655e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5362599015639655e-07
[LightGBM] [Warning] lambda_l1 is set=7.05666808487111e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.05666808487111e-08
[LightGBM] [Warning] bagging_fraction is set=0.4937538265157529, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4937538265157529
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8128029103741291, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8128029103741291
[LightGBM] [Warning] lambda_l2 is set=1.5362599015639655e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5362599015639655e-07
[LightGBM] [Warning] lambda_l1 is set=7.05666808487111e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.05666808487111e-08
[LightGBM] [Warning] bagging_fraction is set=0.4937538265157529, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4937538265157529
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8128029103741291, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8128029103741291
[LightGBM] [Warning] lambda_l2 is set=1.5362599015639655e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.5362599015639655e-07
[LightGBM] [Warning] lambda_l1 is set=7.05666808487111e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.05666808487111e-08
[LightGBM] [Warning] bagging_fraction is set=0.4937538265157529, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4937538265157529
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8479011450235932, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8479011450235932
[LightGBM] [Warning] lambda_l2 is set=7.384522954990507e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.384522954990507e-08
[LightGBM] [Warning] lambda_l1 is set=2.6330321161642624e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.6330321161642624e-08
[LightGBM] [Warning] bagging_fraction is set=0.4340110853684641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4340110853684641
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8479011450235932, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8479011450235932
[LightGBM] [Warning] lambda_l2 is set=7.384522954990507e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.384522954990507e-08
[LightGBM] [Warning] lambda_l1 is set=2.6330321161642624e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.6330321161642624e-08
[LightGBM] [Warning] bagging_fraction is set=0.4340110853684641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4340110853684641
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.8479011450235932, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8479011450235932
[LightGBM] [Warning] lambda_l2 is set=7.384522954990507e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.384522954990507e-08
[LightGBM] [Warning] lambda_l1 is set=2.6330321161642624e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.6330321161642624e-08
[LightGBM] [Warning] bagging_fraction is set=0.4340110853684641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4340110853684641

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7608130485879359, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7608130485879359
[LightGBM] [Warning] lambda_l2 is set=1.949630362731946e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.949630362731946e-08
[LightGBM] [Warning] lambda_l1 is set=6.312663343025119e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.312663343025119e-07
[LightGBM] [Warning] bagging_fraction is set=0.47093902420921835, subsample=1.0 will be ignored. Current value: bagging_fraction=0.47093902420921835
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7608130485879359, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7608130485879359
[LightGBM] [Warning] lambda_l2 is set=1.949630362731946e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.949630362731946e-08
[LightGBM] [Warning] lambda_l1 is set=6.312663343025119e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.312663343025119e-07
[LightGBM] [Warning] bagging_fraction is set=0.47093902420921835, subsample=1.0 will be ignored. Current value: bagging_fraction=0.47093902420921835
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.7608130485879359, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7608130485879359
[LightGBM] [Warning] lambda_l2 is set=1.949630362731946e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.949630362731946e-08
[LightGBM] [Warning] lambda_l1 is set=6.312663343025119e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.312663343025119e-07
[LightGBM] [Warning] bagging_fraction is set=0.47093902420921835, subsample=1.0 will be ignored. Current value: bagging_fraction=0.47093902420921835

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8840211914212027, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8840211914212027
[LightGBM] [Warning] lambda_l2 is set=5.79362562249525e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.79362562249525e-07
[LightGBM] [Warning] lambda_l1 is set=1.7775262001562171e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.7775262001562171e-06
[LightGBM] [Warning] bagging_fraction is set=0.543455117629952, subsample=1.0 will be ignored. Current value: bagging_fraction=0.543455117629952
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8840211914212027, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8840211914212027
[LightGBM] [Warning] lambda_l2 is set=5.79362562249525e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.79362562249525e-07
[LightGBM] [Warning] lambda_l1 is set=1.7775262001562171e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.7775262001562171e-06
[LightGBM] [Warning] bagging_fraction is set=0.543455117629952, subsample=1.0 will be ignored. Current value: bagging_fraction=0.543455117629952
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8840211914212027, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8840211914212027
[LightGBM] [Warning] lambda_l2 is set=5.79362562249525e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.79362562249525e-07
[LightGBM] [Warning] lambda_l1 is set=1.7775262001562171e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.7775262001562171e-06
[LightGBM] [Warning] bagging_fraction is set=0.543455117629952, subsample=1.0 will be ignored. Current value: bagging_fraction=0.543455117629952

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8265123796259073, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8265123796259073
[LightGBM] [Warning] lambda_l2 is set=3.058047444165421e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.058047444165421e-08
[LightGBM] [Warning] lambda_l1 is set=4.1761340166038595e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.1761340166038595e-07
[LightGBM] [Warning] bagging_fraction is set=0.5210201831715455, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5210201831715455
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8265123796259073, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8265123796259073
[LightGBM] [Warning] lambda_l2 is set=3.058047444165421e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.058047444165421e-08
[LightGBM] [Warning] lambda_l1 is set=4.1761340166038595e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.1761340166038595e-07
[LightGBM] [Warning] bagging_fraction is set=0.5210201831715455, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5210201831715455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8265123796259073, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8265123796259073
[LightGBM] [Warning] lambda_l2 is set=3.058047444165421e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.058047444165421e-08
[LightGBM] [Warning] lambda_l1 is set=4.1761340166038595e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.1761340166038595e-07
[LightGBM] [Warning] bagging_fraction is set=0.5210201831715455, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5210201831715455

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7908548009323233, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7908548009323233
[LightGBM] [Warning] lambda_l2 is set=3.33874005582447e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.33874005582447e-08
[LightGBM] [Warning] lambda_l1 is set=3.430735836359869e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.430735836359869e-07
[LightGBM] [Warning] bagging_fraction is set=0.5185688181412853, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5185688181412853
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7908548009323233, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7908548009323233
[LightGBM] [Warning] lambda_l2 is set=3.33874005582447e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.33874005582447e-08
[LightGBM] [Warning] lambda_l1 is set=3.430735836359869e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.430735836359869e-07
[LightGBM] [Warning] bagging_fraction is set=0.5185688181412853, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5185688181412853
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7908548009323233, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7908548009323233
[LightGBM] [Warning] lambda_l2 is set=3.33874005582447e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.33874005582447e-08
[LightGBM] [Warning] lambda_l1 is set=3.430735836359869e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.430735836359869e-07
[LightGBM] [Warning] bagging_fraction is set=0.5185688181412853, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5185688181412853

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7218434882786804, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7218434882786804
[LightGBM] [Warning] lambda_l2 is set=5.746404869777134e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.746404869777134e-08
[LightGBM] [Warning] lambda_l1 is set=1.633211346050851e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.633211346050851e-07
[LightGBM] [Warning] bagging_fraction is set=0.5682380795953521, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5682380795953521
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7218434882786804, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7218434882786804
[LightGBM] [Warning] lambda_l2 is set=5.746404869777134e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.746404869777134e-08
[LightGBM] [Warning] lambda_l1 is set=1.633211346050851e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.633211346050851e-07
[LightGBM] [Warning] bagging_fraction is set=0.5682380795953521, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5682380795953521
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000504 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7218434882786804, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7218434882786804
[LightGBM] [Warning] lambda_l2 is set=5.746404869777134e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.746404869777134e-08
[LightGBM] [Warning] lambda_l1 is set=1.633211346050851e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.633211346050851e-07
[LightGBM] [Warning] bagging_fraction is set=0.5682380795953521, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5682380795953521

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8676929739869441, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8676929739869441
[LightGBM] [Warning] lambda_l2 is set=1.8092137865413777e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8092137865413777e-07
[LightGBM] [Warning] lambda_l1 is set=1.0991000276311988e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0991000276311988e-07
[LightGBM] [Warning] bagging_fraction is set=0.46712447938803997, subsample=1.0 will be ignored. Current value: bagging_fraction=0.46712447938803997
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8676929739869441, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8676929739869441
[LightGBM] [Warning] lambda_l2 is set=1.8092137865413777e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8092137865413777e-07
[LightGBM] [Warning] lambda_l1 is set=1.0991000276311988e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0991000276311988e-07
[LightGBM] [Warning] bagging_fraction is set=0.46712447938803997, subsample=1.0 will be ignored. Current value: bagging_fraction=0.46712447938803997
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8676929739869441, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8676929739869441
[LightGBM] [Warning] lambda_l2 is set=1.8092137865413777e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.8092137865413777e-07
[LightGBM] [Warning] lambda_l1 is set=1.0991000276311988e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0991000276311988e-07
[LightGBM] [Warning] bagging_fraction is set=0.46712447938803997, subsample=1.0 will be ignored. Current value: bagging_fraction=0.46712447938803997

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8333327149383226, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333327149383226
[LightGBM] [Warning] lambda_l2 is set=1.7710291449855196e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7710291449855196e-08
[LightGBM] [Warning] lambda_l1 is set=4.5356902817214624e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.5356902817214624e-08
[LightGBM] [Warning] bagging_fraction is set=0.40658106957217255, subsample=1.0 will be ignored. Current value: bagging_fraction=0.40658106957217255
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8333327149383226, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333327149383226
[LightGBM] [Warning] lambda_l2 is set=1.7710291449855196e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7710291449855196e-08
[LightGBM] [Warning] lambda_l1 is set=4.5356902817214624e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.5356902817214624e-08
[LightGBM] [Warning] bagging_fraction is set=0.40658106957217255, subsample=1.0 will be ignored. Current value: bagging_fraction=0.40658106957217255
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8333327149383226, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333327149383226
[LightGBM] [Warning] lambda_l2 is set=1.7710291449855196e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7710291449855196e-08
[LightGBM] [Warning] lambda_l1 is set=4.5356902817214624e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.5356902817214624e-08
[LightGBM] [Warning] bagging_fraction is set=0.40658106957217255, subsample=1.0 will be ignored. Current value: bagging_fraction=0.40658106957217255

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.9082446424724538, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9082446424724538
[LightGBM] [Warning] lambda_l2 is set=8.795440427034022e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.795440427034022e-08
[LightGBM] [Warning] lambda_l1 is set=1.1507798699865737e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1507798699865737e-08
[LightGBM] [Warning] bagging_fraction is set=0.5062673165160652, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5062673165160652
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.9082446424724538, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9082446424724538
[LightGBM] [Warning] lambda_l2 is set=8.795440427034022e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.795440427034022e-08
[LightGBM] [Warning] lambda_l1 is set=1.1507798699865737e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1507798699865737e-08
[LightGBM] [Warning] bagging_fraction is set=0.5062673165160652, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5062673165160652
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000818 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.9082446424724538, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9082446424724538
[LightGBM] [Warning] lambda_l2 is set=8.795440427034022e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.795440427034022e-08
[LightGBM] [Warning] lambda_l1 is set=1.1507798699865737e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.1507798699865737e-08
[LightGBM] [Warning] bagging_fraction is set=0.5062673165160652, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5062673165160652

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.75867837304164, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.75867837304164
[LightGBM] [Warning] lambda_l2 is set=3.670845827764493e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.670845827764493e-08
[LightGBM] [Warning] lambda_l1 is set=4.078055763783788e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.078055763783788e-07
[LightGBM] [Warning] bagging_fraction is set=0.4478830200454046, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4478830200454046
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.75867837304164, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.75867837304164
[LightGBM] [Warning] lambda_l2 is set=3.670845827764493e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.670845827764493e-08
[LightGBM] [Warning] lambda_l1 is set=4.078055763783788e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.078055763783788e-07
[LightGBM] [Warning] bagging_fraction is set=0.4478830200454046, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4478830200454046
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8
[LightGBM] [Warning] feature_fraction is set=0.75867837304164, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.75867837304164
[LightGBM] [Warning] lambda_l2 is set=3.670845827764493e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.670845827764493e-08
[LightGBM] [Warning] lambda_l1 is set=4.078055763783788e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.078055763783788e-07
[LightGBM] [Warning] bagging_fraction is set=0.4478830200454046, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4478830200454046

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8045161770133857, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8045161770133857
[LightGBM] [Warning] lambda_l2 is set=3.800517394511032e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.800517394511032e-07
[LightGBM] [Warning] lambda_l1 is set=2.1405018860095956e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1405018860095956e-07
[LightGBM] [Warning] bagging_fraction is set=0.5348122501693634, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5348122501693634
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8045161770133857, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8045161770133857
[LightGBM] [Warning] lambda_l2 is set=3.800517394511032e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.800517394511032e-07
[LightGBM] [Warning] lambda_l1 is set=2.1405018860095956e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1405018860095956e-07
[LightGBM] [Warning] bagging_fraction is set=0.5348122501693634, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5348122501693634
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8045161770133857, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8045161770133857
[LightGBM] [Warning] lambda_l2 is set=3.800517394511032e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.800517394511032e-07
[LightGBM] [Warning] lambda_l1 is set=2.1405018860095956e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.1405018860095956e-07
[LightGBM] [Warning] bagging_fraction is set=0.5348122501693634, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5348122501693634

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8031530157655599, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8031530157655599
[LightGBM] [Warning] lambda_l2 is set=1.788796293824674e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.788796293824674e-07
[LightGBM] [Warning] lambda_l1 is set=3.7953631033242813e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.7953631033242813e-06
[LightGBM] [Warning] bagging_fraction is set=0.5513678126745033, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5513678126745033
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8031530157655599, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8031530157655599
[LightGBM] [Warning] lambda_l2 is set=1.788796293824674e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.788796293824674e-07
[LightGBM] [Warning] lambda_l1 is set=3.7953631033242813e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.7953631033242813e-06
[LightGBM] [Warning] bagging_fraction is set=0.5513678126745033, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5513678126745033
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8031530157655599, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8031530157655599
[LightGBM] [Warning] lambda_l2 is set=1.788796293824674e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.788796293824674e-07
[LightGBM] [Warning] lambda_l1 is set=3.7953631033242813e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.7953631033242813e-06
[LightGBM] [Warning] bagging_fraction is set=0.5513678126745033, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5513678126745033

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9449247619180412, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9449247619180412
[LightGBM] [Warning] lambda_l2 is set=1.7993402729178672e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7993402729178672e-08
[LightGBM] [Warning] lambda_l1 is set=7.913263414068691e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.913263414068691e-07
[LightGBM] [Warning] bagging_fraction is set=0.629968298889712, subsample=1.0 will be ignored. Current value: bagging_fraction=0.629968298889712
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9449247619180412, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9449247619180412
[LightGBM] [Warning] lambda_l2 is set=1.7993402729178672e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7993402729178672e-08
[LightGBM] [Warning] lambda_l1 is set=7.913263414068691e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.913263414068691e-07
[LightGBM] [Warning] bagging_fraction is set=0.629968298889712, subsample=1.0 will be ignored. Current value: bagging_fraction=0.629968298889712
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9449247619180412, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9449247619180412
[LightGBM] [Warning] lambda_l2 is set=1.7993402729178672e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7993402729178672e-08
[LightGBM] [Warning] lambda_l1 is set=7.913263414068691e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.913263414068691e-07
[LightGBM] [Warning] bagging_fraction is set=0.629968298889712, subsample=1.0 will be ignored. Current value: bagging_fraction=0.629968298889712

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.976525864917137, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.976525864917137
[LightGBM] [Warning] lambda_l2 is set=1.7488081988251008e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7488081988251008e-08
[LightGBM] [Warning] lambda_l1 is set=7.675897533670365e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.675897533670365e-07
[LightGBM] [Warning] bagging_fraction is set=0.6348173284792311, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6348173284792311
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.976525864917137, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.976525864917137
[LightGBM] [Warning] lambda_l2 is set=1.7488081988251008e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7488081988251008e-08
[LightGBM] [Warning] lambda_l1 is set=7.675897533670365e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.675897533670365e-07
[LightGBM] [Warning] bagging_fraction is set=0.6348173284792311, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6348173284792311
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.976525864917137, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.976525864917137
[LightGBM] [Warning] lambda_l2 is set=1.7488081988251008e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.7488081988251008e-08
[LightGBM] [Warning] lambda_l1 is set=7.675897533670365e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.675897533670365e-07
[LightGBM] [Warning] bagging_fraction is set=0.6348173284792311, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6348173284792311

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9434930811815107, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9434930811815107
[LightGBM] [Warning] lambda_l2 is set=4.108058910492359e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.108058910492359e-08
[LightGBM] [Warning] lambda_l1 is set=1.5244800420196376e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5244800420196376e-06
[LightGBM] [Warning] bagging_fraction is set=0.6073375420649857, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6073375420649857
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9434930811815107, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9434930811815107
[LightGBM] [Warning] lambda_l2 is set=4.108058910492359e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.108058910492359e-08
[LightGBM] [Warning] lambda_l1 is set=1.5244800420196376e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5244800420196376e-06
[LightGBM] [Warning] bagging_fraction is set=0.6073375420649857, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6073375420649857
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5
[LightGBM] [Warning] feature_fraction is set=0.9434930811815107, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9434930811815107
[LightGBM] [Warning] lambda_l2 is set=4.108058910492359e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.108058910492359e-08
[LightGBM] [Warning] lambda_l1 is set=1.5244800420196376e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5244800420196376e-06
[LightGBM] [Warning] bagging_fraction is set=0.6073375420649857, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6073375420649857

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.99938503515253, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.99938503515253
[LightGBM] [Warning] lambda_l2 is set=2.502803390982526e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.502803390982526e-08
[LightGBM] [Warning] lambda_l1 is set=7.488019537032656e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.488019537032656e-07
[LightGBM] [Warning] bagging_fraction is set=0.6487710442917833, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6487710442917833
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.99938503515253, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.99938503515253
[LightGBM] [Warning] lambda_l2 is set=2.502803390982526e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.502803390982526e-08
[LightGBM] [Warning] lambda_l1 is set=7.488019537032656e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.488019537032656e-07
[LightGBM] [Warning] bagging_fraction is set=0.6487710442917833, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6487710442917833
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.99938503515253, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.99938503515253
[LightGBM] [Warning] lambda_l2 is set=2.502803390982526e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.502803390982526e-08
[LightGBM] [Warning] lambda_l1 is set=7.488019537032656e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.488019537032656e-07
[LightGBM] [Warning] bagging_fraction is set=0.6487710442917833, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6487710442917833

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.9050204833295874, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9050204833295874
[LightGBM] [Warning] lambda_l2 is set=1.4180863211545687e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4180863211545687e-08
[LightGBM] [Warning] lambda_l1 is set=4.149553648969754e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.149553648969754e-07
[LightGBM] [Warning] bagging_fraction is set=0.6821709573955783, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6821709573955783
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.9050204833295874, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9050204833295874
[LightGBM] [Warning] lambda_l2 is set=1.4180863211545687e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4180863211545687e-08
[LightGBM] [Warning] lambda_l1 is set=4.149553648969754e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.149553648969754e-07
[LightGBM] [Warning] bagging_fraction is set=0.6821709573955783, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6821709573955783
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6
[LightGBM] [Warning] feature_fraction is set=0.9050204833295874, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9050204833295874
[LightGBM] [Warning] lambda_l2 is set=1.4180863211545687e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4180863211545687e-08
[LightGBM] [Warning] lambda_l1 is set=4.149553648969754e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.149553648969754e-07
[LightGBM] [Warning] bagging_fraction is set=0.6821709573955783, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6821709573955783

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9320464063582653, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9320464063582653
[LightGBM] [Warning] lambda_l2 is set=1.0195121477603592e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0195121477603592e-08
[LightGBM] [Warning] lambda_l1 is set=1.230043374394851e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.230043374394851e-07
[LightGBM] [Warning] bagging_fraction is set=0.5824191297797673, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5824191297797673
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9320464063582653, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9320464063582653
[LightGBM] [Warning] lambda_l2 is set=1.0195121477603592e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0195121477603592e-08
[LightGBM] [Warning] lambda_l1 is set=1.230043374394851e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.230043374394851e-07
[LightGBM] [Warning] bagging_fraction is set=0.5824191297797673, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5824191297797673
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9320464063582653, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9320464063582653
[LightGBM] [Warning] lambda_l2 is set=1.0195121477603592e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0195121477603592e-08
[LightGBM] [Warning] lambda_l1 is set=1.230043374394851e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.230043374394851e-07
[LightGBM] [Warning] bagging_fraction is set=0.5824191297797673, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5824191297797673

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.871263278031558, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.871263278031558
[LightGBM] [Warning] lambda_l2 is set=8.932801091509175e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.932801091509175e-08
[LightGBM] [Warning] lambda_l1 is set=7.164705403114211e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.164705403114211e-08
[LightGBM] [Warning] bagging_fraction is set=0.6028617705138285, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6028617705138285
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.871263278031558, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.871263278031558
[LightGBM] [Warning] lambda_l2 is set=8.932801091509175e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.932801091509175e-08
[LightGBM] [Warning] lambda_l1 is set=7.164705403114211e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.164705403114211e-08
[LightGBM] [Warning] bagging_fraction is set=0.6028617705138285, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6028617705138285
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=7, subsample_freq=0 will be ignored. Current value: bagging_freq=7
[LightGBM] [Warning] feature_fraction is set=0.871263278031558, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.871263278031558
[LightGBM] [Warning] lambda_l2 is set=8.932801091509175e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=8.932801091509175e-08
[LightGBM] [Warning] lambda_l1 is set=7.164705403114211e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.164705403114211e-08
[LightGBM] [Warning] bagging_fraction is set=0.6028617705138285, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6028617705138285

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8435349567432106, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8435349567432106
[LightGBM] [Warning] lambda_l2 is set=4.554722696830233e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.554722696830233e-08
[LightGBM] [Warning] lambda_l1 is set=1.6012278714144155e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6012278714144155e-06
[LightGBM] [Warning] bagging_fraction is set=0.5663093674860844, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5663093674860844
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8435349567432106, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8435349567432106
[LightGBM] [Warning] lambda_l2 is set=4.554722696830233e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.554722696830233e-08
[LightGBM] [Warning] lambda_l1 is set=1.6012278714144155e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6012278714144155e-06
[LightGBM] [Warning] bagging_fraction is set=0.5663093674860844, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5663093674860844
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8435349567432106, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8435349567432106
[LightGBM] [Warning] lambda_l2 is set=4.554722696830233e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=4.554722696830233e-08
[LightGBM] [Warning] lambda_l1 is set=1.6012278714144155e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.6012278714144155e-06
[LightGBM] [Warning] bagging_fraction is set=0.5663093674860844, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5663093674860844

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9634753402343621, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9634753402343621
[LightGBM] [Warning] lambda_l2 is set=2.557615308798482e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.557615308798482e-08
[LightGBM] [Warning] lambda_l1 is set=7.724877436687031e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.724877436687031e-07
[LightGBM] [Warning] bagging_fraction is set=0.4867654717017151, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4867654717017151
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9634753402343621, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9634753402343621
[LightGBM] [Warning] lambda_l2 is set=2.557615308798482e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.557615308798482e-08
[LightGBM] [Warning] lambda_l1 is set=7.724877436687031e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.724877436687031e-07
[LightGBM] [Warning] bagging_fraction is set=0.4867654717017151, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4867654717017151
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9634753402343621, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9634753402343621
[LightGBM] [Warning] lambda_l2 is set=2.557615308798482e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.557615308798482e-08
[LightGBM] [Warning] lambda_l1 is set=7.724877436687031e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.724877436687031e-07
[LightGBM] [Warning] bagging_fraction is set=0.4867654717017151, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4867654717017151

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.9146477649582616, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9146477649582616
[LightGBM] [Warning] lambda_l2 is set=7.128923074653517e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.128923074653517e-08
[LightGBM] [Warning] lambda_l1 is set=3.414348513084997e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.414348513084997e-07
[LightGBM] [Warning] bagging_fraction is set=0.5189553172656485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5189553172656485
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.9146477649582616, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9146477649582616
[LightGBM] [Warning] lambda_l2 is set=7.128923074653517e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.128923074653517e-08
[LightGBM] [Warning] lambda_l1 is set=3.414348513084997e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.414348513084997e-07
[LightGBM] [Warning] bagging_fraction is set=0.5189553172656485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5189553172656485
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000470 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.9146477649582616, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9146477649582616
[LightGBM] [Warning] lambda_l2 is set=7.128923074653517e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.128923074653517e-08
[LightGBM] [Warning] lambda_l1 is set=3.414348513084997e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=3.414348513084997e-07
[LightGBM] [Warning] bagging_fraction is set=0.5189553172656485, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5189553172656485

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8168271196749461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8168271196749461
[LightGBM] [Warning] lambda_l2 is set=1.6737325409927277e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.6737325409927277e-08
[LightGBM] [Warning] lambda_l1 is set=2.2319694205973847e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2319694205973847e-07
[LightGBM] [Warning] bagging_fraction is set=0.5363936022966763, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5363936022966763
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8168271196749461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8168271196749461
[LightGBM] [Warning] lambda_l2 is set=1.6737325409927277e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.6737325409927277e-08
[LightGBM] [Warning] lambda_l1 is set=2.2319694205973847e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2319694205973847e-07
[LightGBM] [Warning] bagging_fraction is set=0.5363936022966763, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5363936022966763
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8168271196749461, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8168271196749461
[LightGBM] [Warning] lambda_l2 is set=1.6737325409927277e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.6737325409927277e-08
[LightGBM] [Warning] lambda_l1 is set=2.2319694205973847e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2319694205973847e-07
[LightGBM] [Warning] bagging_fraction is set=0.5363936022966763, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5363936022966763

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7811193757033942, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7811193757033942
[LightGBM] [Warning] lambda_l2 is set=3.435800432549481e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.435800432549481e-07
[LightGBM] [Warning] lambda_l1 is set=1.5513869081585333e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5513869081585333e-07
[LightGBM] [Warning] bagging_fraction is set=0.6234120080219813, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6234120080219813
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7811193757033942, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7811193757033942
[LightGBM] [Warning] lambda_l2 is set=3.435800432549481e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.435800432549481e-07
[LightGBM] [Warning] lambda_l1 is set=1.5513869081585333e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5513869081585333e-07
[LightGBM] [Warning] bagging_fraction is set=0.6234120080219813, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6234120080219813
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7811193757033942, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7811193757033942
[LightGBM] [Warning] lambda_l2 is set=3.435800432549481e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.435800432549481e-07
[LightGBM] [Warning] lambda_l1 is set=1.5513869081585333e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.5513869081585333e-07
[LightGBM] [Warning] bagging_fraction is set=0.6234120080219813, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6234120080219813

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8898738062462264, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8898738062462264
[LightGBM] [Warning] lambda_l2 is set=1.9335985885704643e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.9335985885704643e-07
[LightGBM] [Warning] lambda_l1 is set=5.167103641087981e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.167103641087981e-07
[LightGBM] [Warning] bagging_fraction is set=0.50166651951252, subsample=1.0 will be ignored. Current value: bagging_fraction=0.50166651951252
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8898738062462264, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8898738062462264
[LightGBM] [Warning] lambda_l2 is set=1.9335985885704643e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.9335985885704643e-07
[LightGBM] [Warning] lambda_l1 is set=5.167103641087981e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.167103641087981e-07
[LightGBM] [Warning] bagging_fraction is set=0.50166651951252, subsample=1.0 will be ignored. Current value: bagging_fraction=0.50166651951252
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8898738062462264, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8898738062462264
[LightGBM] [Warning] lambda_l2 is set=1.9335985885704643e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.9335985885704643e-07
[LightGBM] [Warning] lambda_l1 is set=5.167103641087981e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=5.167103641087981e-07
[LightGBM] [Warning] bagging_fraction is set=0.50166651951252, subsample=1.0 will be ignored. Current value: bagging_fraction=0.50166651951252

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7550926388689168, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7550926388689168
[LightGBM] [Warning] lambda_l2 is set=1.0293047879337473e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0293047879337473e-07
[LightGBM] [Warning] lambda_l1 is set=4.055977315635005e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.055977315635005e-08
[LightGBM] [Warning] bagging_fraction is set=0.5910381871713358, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5910381871713358
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7550926388689168, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7550926388689168
[LightGBM] [Warning] lambda_l2 is set=1.0293047879337473e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0293047879337473e-07
[LightGBM] [Warning] lambda_l1 is set=4.055977315635005e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.055977315635005e-08
[LightGBM] [Warning] bagging_fraction is set=0.5910381871713358, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5910381871713358
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7550926388689168, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7550926388689168
[LightGBM] [Warning] lambda_l2 is set=1.0293047879337473e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0293047879337473e-07
[LightGBM] [Warning] lambda_l1 is set=4.055977315635005e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=4.055977315635005e-08
[LightGBM] [Warning] bagging_fraction is set=0.5910381871713358, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5910381871713358
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8599765391829325, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8599765391829325
[LightGBM] [Warning] lambda_l2 is set=3.881933945360297e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.881933945360297e-08
[LightGBM] [Warning] lambda_l1 is set=2.2235054114904914e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2235054114904914e-07
[LightGBM] [Warning] bagging_fraction is set=0.5572734185663807, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5572734185663807
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8599765391829325, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8599765391829325
[LightGBM] [Warning] lambda_l2 is set=3.881933945360297e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.881933945360297e-08
[LightGBM] [Warning] lambda_l1 is set=2.2235054114904914e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2235054114904914e-07
[LightGBM] [Warning] bagging_fraction is set=0.5572734185663807, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5572734185663807
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.8599765391829325, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8599765391829325
[LightGBM] [Warning] lambda_l2 is set=3.881933945360297e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=3.881933945360297e-08
[LightGBM] [Warning] lambda_l1 is set=2.2235054114904914e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.2235054114904914e-07
[LightGBM] [Warning] bagging_fraction is set=0.5572734185663807, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5572734185663807

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8291668277295507, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8291668277295507
[LightGBM] [Warning] lambda_l2 is set=2.8489682209374852e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.8489682209374852e-08
[LightGBM] [Warning] lambda_l1 is set=7.958029885568782e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.958029885568782e-08
[LightGBM] [Warning] bagging_fraction is set=0.4741842773492985, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4741842773492985
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8291668277295507, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8291668277295507
[LightGBM] [Warning] lambda_l2 is set=2.8489682209374852e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.8489682209374852e-08
[LightGBM] [Warning] lambda_l1 is set=7.958029885568782e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.958029885568782e-08
[LightGBM] [Warning] bagging_fraction is set=0.4741842773492985, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4741842773492985
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.8291668277295507, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8291668277295507
[LightGBM] [Warning] lambda_l2 is set=2.8489682209374852e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.8489682209374852e-08
[LightGBM] [Warning] lambda_l1 is set=7.958029885568782e-08, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.958029885568782e-08
[LightGBM] [Warning] bagging_fraction is set=0.4741842773492985, subsample=1.0 will be ignored. Current value: bagging_fraction=0.4741842773492985

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7898716481953018, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7898716481953018
[LightGBM] [Warning] lambda_l2 is set=1.4983126558162703e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4983126558162703e-08
[LightGBM] [Warning] lambda_l1 is set=1.0350806014120941e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0350806014120941e-06
[LightGBM] [Warning] bagging_fraction is set=0.5234618403171056, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5234618403171056
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7898716481953018, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7898716481953018
[LightGBM] [Warning] lambda_l2 is set=1.4983126558162703e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4983126558162703e-08
[LightGBM] [Warning] lambda_l1 is set=1.0350806014120941e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0350806014120941e-06
[LightGBM] [Warning] bagging_fraction is set=0.5234618403171056, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5234618403171056
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.7898716481953018, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7898716481953018
[LightGBM] [Warning] lambda_l2 is set=1.4983126558162703e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.4983126558162703e-08
[LightGBM] [Warning] lambda_l1 is set=1.0350806014120941e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=1.0350806014120941e-06
[LightGBM] [Warning] bagging_fraction is set=0.5234618403171056, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5234618403171056

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7162692571089988, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7162692571089988
[LightGBM] [Warning] lambda_l2 is set=5.480591549827026e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.480591549827026e-08
[LightGBM] [Warning] lambda_l1 is set=2.3829262519377185e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.3829262519377185e-06
[LightGBM] [Warning] bagging_fraction is set=0.6617787348994468, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6617787348994468
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7162692571089988, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7162692571089988
[LightGBM] [Warning] lambda_l2 is set=5.480591549827026e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.480591549827026e-08
[LightGBM] [Warning] lambda_l1 is set=2.3829262519377185e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.3829262519377185e-06
[LightGBM] [Warning] bagging_fraction is set=0.6617787348994468, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6617787348994468
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.7162692571089988, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7162692571089988
[LightGBM] [Warning] lambda_l2 is set=5.480591549827026e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.480591549827026e-08
[LightGBM] [Warning] lambda_l1 is set=2.3829262519377185e-06, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.3829262519377185e-06
[LightGBM] [Warning] bagging_fraction is set=0.6617787348994468, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6617787348994468

/tmp/ipykernel_20710/1047994459.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:8: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
/tmp/ipykernel_20710/1047994459.py:10: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'feature_fraction': trial.suggest_uniform('feature_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:11: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.
  'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.1, 1.0),
/tmp/ipykernel_20710/1047994459.py:14: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
  'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.3),

[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8077541096230938, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8077541096230938
[LightGBM] [Warning] lambda_l2 is set=1.0997800158789703e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0997800158789703e-07
[LightGBM] [Warning] lambda_l1 is set=2.814292798467685e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.814292798467685e-07
[LightGBM] [Warning] bagging_fraction is set=0.5671669739851747, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5671669739851747
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8077541096230938, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8077541096230938
[LightGBM] [Warning] lambda_l2 is set=1.0997800158789703e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0997800158789703e-07
[LightGBM] [Warning] lambda_l1 is set=2.814292798467685e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.814292798467685e-07
[LightGBM] [Warning] bagging_fraction is set=0.5671669739851747, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5671669739851747
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 13
[LightGBM] [Info] Start training from score 3115.048272
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] feature_fraction is set=0.8077541096230938, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8077541096230938
[LightGBM] [Warning] lambda_l2 is set=1.0997800158789703e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=1.0997800158789703e-07
[LightGBM] [Warning] lambda_l1 is set=2.814292798467685e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=2.814292798467685e-07
[LightGBM] [Warning] bagging_fraction is set=0.5671669739851747, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5671669739851747
In [119]:
# Imprimindo os melhores parâmetros segundo o Optuna
best_params = study.best_params
print("Melhores Parâmetros:")
print(best_params)

Melhores Parâmetros:
{'lambda_l1': 7.709653786630755e-07, 'lambda_l2': 2.0670041424444295e-08, 'num_leaves': 221, 'feature_fraction': 0.9439758799807783, 'bagging_fraction': 0.5970426207694153, 'bagging_freq': 3, 'min_child_samples': 8, 'learning_rate': 0.23266840345811338, 'n_estimators': 674}
In [120]:
# Criando o modelo final com os melhores parâmetros
final_model = lgb.LGBMRegressor(**best_params)
final_model.fit(treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp])

[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 11228, number of used features: 14
[LightGBM] [Info] Start training from score 3115.048272
Out[120]:
LGBMRegressor(bagging_fraction=0.5970426207694153, bagging_freq=3,
              feature_fraction=0.9439758799807783,
              lambda_l1=7.709653786630755e-07, lambda_l2=2.0670041424444295e-08,
              learning_rate=0.23266840345811338, min_child_samples=8,
              n_estimators=674, num_leaves=221)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LGBMRegressor(bagging_fraction=0.5970426207694153, bagging_freq=3,
              feature_fraction=0.9439758799807783,
              lambda_l1=7.709653786630755e-07, lambda_l2=2.0670041424444295e-08,
              learning_rate=0.23266840345811338, min_child_samples=8,
              n_estimators=674, num_leaves=221)
In [121]:
#CONJUNTO TREINO

y_pred_final_treino = final_model.predict(treino_base_aluguel[vars_exp])

rmse_final_treino = np.sqrt(mean_squared_error(treino_base_aluguel[vars_resp], y_pred_final_treino))

print(f"Treino - RMSE do Modelo Final: {rmse_final_treino}")

#CONJUNTO TESTE

# Previsões com o modelo final
y_pred_final = final_model.predict(teste_base_aluguel[vars_exp])

# Calculando a métrica RMSE com o modelo final
rmse_final = np.sqrt(mean_squared_error(teste_base_aluguel[vars_resp], y_pred_final))
print(f"Teste - RMSE do Modelo Final: {rmse_final}")

[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
Treino - RMSE do Modelo Final: 153.9903276944512
[LightGBM] [Warning] feature_fraction is set=0.9439758799807783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9439758799807783
[LightGBM] [Warning] lambda_l2 is set=2.0670041424444295e-08, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.0670041424444295e-08
[LightGBM] [Warning] lambda_l1 is set=7.709653786630755e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=7.709653786630755e-07
[LightGBM] [Warning] bagging_fraction is set=0.5970426207694153, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5970426207694153
[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3
Teste - RMSE do Modelo Final: 902.2737636948696
In [122]:
# Verificando a importância relativa de cada variável

importancias_variaveis = final_model.feature_importances_

df_aluguel_lgbm = pd.DataFrame({'Variável': vars_exp, 'Coeficiente': importancias_variaveis})
df_aluguel_lgbm.sort_values(by='Coeficiente', ascending=False)
Out[122]:
Variável Coeficiente
1 size 49571
0 condo 47394
3 toilets 6929
2 rooms 6816
6 elevator 6013
8 swimming_pool 5798
5 parking 5299
11 district_zone_Leste 4578
13 district_zone_Oeste 4344
7 furnished 3776
4 suites 3058
12 district_zone_Norte 2928
10 district_zone_Centro 1770
9 new 6

Comentários: as variáveis com mais importância (que mais foram utilizadas para tomar decisão) foram valor do condomínio, tamanho da propriedade e número de quartos. As variáveis que menos contribuiram foram se a propriedade é nova ou não

In [123]:
# Gráfico de dispersão

GraficoDispersao(teste_base_aluguel[vars_resp], y_pred_final, 'Gráfico de Dispersão entre Valores Reais e Previstos', 'Valores Reais', 'Valores Previstos')

Cross - Validation¶

In [124]:
lgbm_reg = lgb.LGBMRegressor()
lgbm_scores = cross_val_score(lgbm_reg, treino_base_aluguel[vars_exp], treino_base_aluguel[vars_resp], scoring="neg_mean_squared_error", cv=10)

lgbm_rmse_scores = np.sqrt(-lgbm_scores)

display_scores(lgbm_rmse_scores)

[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 546
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 12
[LightGBM] [Info] Start training from score 3135.822662
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 3257.042454
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 549
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 3253.037803
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 551
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 3261.561306
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 550
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 3261.762494
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 545
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 2968.363088
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 544
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 2943.736566
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 543
[LightGBM] [Info] Number of data points in the train set: 10105, number of used features: 13
[LightGBM] [Info] Start training from score 3004.979218
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000442 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 546
[LightGBM] [Info] Number of data points in the train set: 10106, number of used features: 13
[LightGBM] [Info] Start training from score 3033.976944
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000419 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 542
[LightGBM] [Info] Number of data points in the train set: 10106, number of used features: 13
[LightGBM] [Info] Start training from score 3030.216604
Scores: [2216.89795156 1202.64591444 1339.59405411  848.2442539   781.77398453
 2629.78164529 2437.06344884 1970.95623685 1999.31226494 2246.28980888]
Mean: 1767.255956333763
Standard deviation: 635.3758226055528
In [125]:
import pickle
pickle.dump(final_model, open('modelo_aluguel.pkl', 'wb'))

Salvando os pkl's no S3 de output¶

In [126]:
import os

notebook_directory = os.getcwd()

# Caminho completo para o arquivo pkl
modelo_venda_path = os.path.join(notebook_directory, 'modelo_venda.pkl')
modelo_aluguel_path = os.path.join(notebook_directory, 'modelo_aluguel.pkl')

# Carregue o modelo a partir do arquivo pkl
with open(modelo_venda_path, 'rb') as file:
    modelo_venda = pickle.load(file)
    
with open(modelo_aluguel_path, 'rb') as file:
    modelo_aluguel = pickle.load(file)    

# Serializar o modelo para um arquivo pickle
modelo_venda_pickle = pickle.dumps(modelo_venda)
modelo_aluguel_pickle = pickle.dumps(modelo_aluguel)


# Configurações do Amazon S3
bucket_name = 'output-modelo'

# Upload do arquivo pickle para o Amazon S3
s3_client = boto3.client('s3')
s3_client.put_object(Body=modelo_venda_pickle, Bucket=bucket_name, Key=modelo_venda_path)
Out[126]:
{'ResponseMetadata': {'RequestId': 'GS9VATGTV1RD21J1',
  'HostId': '/xlmK3tQntJvOFlgbZmLT9F820ISPjughTOoU3AEXVgkH9TvUZQXVGpvtY1wYGKmlbdQgUyX+wBa3DFFgO546A==',
  'HTTPStatusCode': 200,
  'HTTPHeaders': {'x-amz-id-2': '/xlmK3tQntJvOFlgbZmLT9F820ISPjughTOoU3AEXVgkH9TvUZQXVGpvtY1wYGKmlbdQgUyX+wBa3DFFgO546A==',
   'x-amz-request-id': 'GS9VATGTV1RD21J1',
   'date': 'Wed, 06 Dec 2023 23:25:02 GMT',
   'x-amz-server-side-encryption': 'AES256',
   'etag': '"e70f8799a50d30967652c4cf7ac02205"',
   'server': 'AmazonS3',
   'content-length': '0'},
  'RetryAttempts': 0},
 'ETag': '"e70f8799a50d30967652c4cf7ac02205"',
 'ServerSideEncryption': 'AES256'}

📊🔬 Conclusão¶

O modelo de regressão linear foi o que apresentou os maiores erros nas bases de aluguel e venda. Tal comportamento pode ser devido a simplicidade do modelo e pelo fato de algumas propriedades não terem necessariamente uma relação linear e de não termos aplicado nenhum tipo de regularização. Já quando partimos para o Boosting e o LGBM os erros foram menores. O LGBM se mostrou mais estável em relação as diferenças de erros entre as bases de treino e teste. Como os erros foram na mesma ordem de grandeza, não consideramos que houve overfit ou underfit.